Past Presentations

The following presenters have agreed to share their presentations with the MaMLIC group

AI-Driven Multimodal Computational Pathology

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Computational Pathology and Cancer Prediction

Presenter:

Faisal Mahmood

Abstract:

This talk will discuss the use of AI in pathology and methods of training digitized images through the utilization of smaller patches to make the data efficient. Models have been developed to identify the most important images patches. In addition, the development of a breast cancer lymph node metastasis model will be discussed, showing how different imaging approaches were used. Models predicting histology have been developed for cancer applications.

Modality:

  • CT
  • Optical/microscopy
  • Histopathology
  • Genomics

Organ

  • Breast
  • Cardiac
  • Prostate

Disease

  • Cancer

AI-Based Precision Pathology

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Computational Pathology and Cancer Prediction

Presenter:

Mattias Rantalainen

Abstract:

This talk will discuss precision medicine and its use in the development of AI in histopathology for the development of outcome prediction and treatment response prediction models. The development of a risk stratification model for breast cancer histological assessment will be discussed and the development of a medical device.

Modality:

  • Optical/microscopy
  • Gene expression
  • Histopathology

Organ

  • Breast
  • Lung
  • Prostate
  • Skin
  • Colorectal

Disease

  • Cancer

Other

  • Commercialization

Machine learning to predict disease progression

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Predicting Disease Progression

Presenter:

Sun-In Lee

Abstract:

This talk will describe the use of text and phenotypic data in medical records for predicting patients’ clinical course, and potential uses for medical management. The presentation will be followed by questions and answers about the talk, and discussion focused on lessons learned, opportunities to collaborate, and sharing of data.

Modality:

  • Gene expression

Organ

  • Brain
  • Hematopathology
  • Lung
  • Skin

Disease

  • Cancer
  • Kidney and Liver Disease
  • Neurological Disease
  • Stroke and Cardiovascular
  • COVID

Other

  • Therapeutics
  • Personalized medicine

Segment Anything in Medical Images

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Enforcing Geometry in Machine Learning for Computational Neuroimaging

Presenter:

Jun Ma

Abstract:

Medical imaging plays an indispensable role in clinical practice. Accurate and efficient medical image segmentation provides a means of delineating regions of interest and quantifying various clinical metrics. However, building customized segmentation models for each medical imaging task can be a daunting and time-consuming process, limiting the widespread adoption in clinical practice. In this talk, Jun will introduce MedSAM, a segmentation foundation model that enables universal segmentation across a wide range of medical imaging tasks and modalities. MedSAM achieved remarkable improvements in 30 segmentation tasks, surpassing the existing segmentation foundation model by a large margin. MedSAM also demonstrated zero-shot and few-shot capabilities to segment unseen tumor types and adapt to new imaging modalities with minimal effort. The results validate the versatility of MedSAM compared to existing customized segmentation models, emphasizing its potential to transform medical image segmentation and enhance clinical practice.

Disease

  • Cancer

Other

  • Resource Sharing
  • Reproducibility

Artificial intelligence augmented ultrasound detection of hip dysplasia

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on AI and Precision Health Initiatives at the University of Alberta

Presenter:

Jacob Jaremko

Abstract:

These talks will present new efforts underway at the University of Alberta in Edmonton AB to develop and translate AI technology into clinical practice. The College of Health Sciences at UAlberta is leading a new initiative in AI for precision health. These efforts are conducted in collaboration with the Alberta Machine Intelligence Institute (Amii), one of three national AI institutes supported through the Pan-Canadian AI Strategy. Amii is located in Edmonton and supports 40 professors conducting basic and applied AI research. Precision Health is the largest application area within Amii.

The UAlberta College of Health Sciences also collaborates extensively with Alberta Health Services (AHS), Canada’s first and largest province-wide, fully integrated health system. AHS provides health services to over 4.4 million people. For the past 20 years they have developed and maintained a broadly integrated data warehouse to collect and protect health data everywhere they provide services. This allows construction of large population-level datasets for precision health research.

These presentations will discuss some of the projects currently underway, and look ahead at new efforts to expand AI and Precision Health research at UAlberta and beyond.

Modality:

  • US

Organ:

  • Musculoskeletal

Disease

  • Musculoskeletal Disease

Other

  • Commercialization

Sensitivity of convolutional neural networks to common imaging parameters, perturbations and artifacts in MRI

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Alzheimer’s and Small Vessel Disease Imaging

Presenter:

Lyndon Boone

Abstract:

A number of studies have shown that deep learning methods are capable of achieving near-human-level performance on neuroimaging segmentation tasks. With that said, most of the results quoted in the literature supporting this statement are in the context of test sets drawn from the same overarching dataset as the training data. Clinically-deployed models faced with out-of-distribution data (i.e. data that does not resemble the training set) may severely underperform relative to the standards set in the literature if they aren’t designed specifically with robustness to out-of-distribution data in mind. In this talk, Lyndon highlighted the sensitivity of modern CNN-based architectures to image corruptions, artifacts, and post-processing transforms commonly found in MRI. He then presented a methodology for benchmarking different architectures and models on the basis of robustness to out-of-distribution data, inspired by similar work in the computer vision literature.

Modality:

  • MR

Organ:

  • Brain

Deep Learning for Automated Segmentation of Left Ventricle Myocardium and Myocardial Scar From 3-D MR Images

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Machine Learning in Cardiac Imaging

Presenter:

Fatemeh Zabihollahy

Abstract:

Deep learning has demonstrated promise for various cardiac imaging applications. However, the performance is usually degraded when the models are trained with small and under-annotated training datasets and tested on previously unseen domains, limiting the potential for broad clinical use. In this talk, Fumin presented his recent work on combining deep learning and machine learning models for cardiac MRI segmentation, where smaller datasets and fewer annotations are required for algorithm training. He also provided examples of integrating the segmentation tools for myocardial infarct heterogeneity quantification in contrast enhancement MRI in the context of MRI-guided cardiac arrhythmia treatment.

Modality:

  • MR

Organ:

  • Cardiac

Disease

  • Heart Disease
  • Stroke and Cardiovascular

Deep learning with uncertainty quantification in MRI-guided radiation therapy

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Reproducibility and Good Practices in Machine Learning Research

Presenter:

Matt Hemsley

Abstract:

Deep learning methods are able to match or surpass the performance of ​​classical methods as well as human experts in a growing number of medical imaging related tasks. However, most deep learning methods are unable to quantify uncertainty, hindering clinical translation. In this talk, Matt presented an overview of techniques used to model uncertainty on network output, demonstrated the connection between uncertainty quantification and reproducibility, and presented examples where uncertainty estimates were obtained from networks that performed clinical tasks related to real-time MRI-guided adaptive radiation therapy.

Modality:

  • CT
  • MR

Organ:

  • Brain
  • Head and Neck

Disease

  • Cancer

Design, conduct, and reporting of radiomic analyses: Let’s not reinvent the wheel

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Reproducibility and Good Practices in Machine Learning Research

Presenter:

Chaya Moskowitz

Abstract:

The number of published papers reporting on radiomic analyses has been growing exponentially. Very few of these paper produce results that are translated into clinical practice at least in part because of methodological flaws in the work. Although complex methods for producing radiomic signatures are increasingly available and user-friendly and progress has been made in radiomic biomarker taxonomy and standardization, fundamental elements of study design, rigorous statistical analysis, and quality of reporting methods and results are frequently overlooked. In this talk, Chaya highlighted common pitfalls encountered in radiomic studies that could be avoided by knowledge of existing methods and adherence to existing reporting standards.

Other:

  • Reproducibility

Improving Peproducibility in Machine Learning Research

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Reproducibility and Good Practices in Machine Learning Research

Presenter:

Benjamin Haibe-Kains

Abstract:

While biostatistics and machine learning are essential to analyze biomedical data, researchers are facing multiple challenges around research reproducibility and transparency. It is essential for independent researchers to be able to scrutinize and reproduce the results of a study using its materials, and build upon them in future studies.

Computational reproducibility is achievable when the data can easily be shared and the required computational resources are relatively common. However, the complexity of current algorithms and their implementation, the need for specific computer hardware and the use of sensitive biomedical data represent major obstacles in healthy-related research. In this talk, Benjamin described the various aspects of a typical biomedical study that are necessary for reproducibility and the platforms that exist for sharing these materials with the scientific community.

Other:

  • Resource Sharing
  • Reproducibility

Introduction to the Ontario Health Data Platform

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Machine Learning in Health Data Integration Platforms

Presenter:

Amber Simpson

Abstract:

The Ontario Health Data Platform (OHDP) is a secure, federated high-performance computing environment built for linkage and analysis of large health data sets. The platform was built to aid Ontario in their fight against COVID-19. This talk described the platform, available data, and the potential for future data such as genomic and imaging data repositories.

Other:

  • Data source/database

Brain-CODE

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Machine Learning in Health Data Integration Platforms

Presenters:

Tom Mikkelsen, Kirk Nylen, Brendan Behan

Abstract:

The Ontario Brain Institute (OBI) provided an overview of our Brain-CODE platform, its data holdings, and the opportunity to access and analyze highly standardized data on people living with various brain disorders – ranging from pediatric neurodevelopmental disorders through to neurodegenerative diseases of aging. We hoped to engage high-end data scientists like you in a discussion around potential collaboration opportunities with our large network of clinical experts/researchers.\

Modality:

  • MR
  • Gene Expression

Organ:

  • Brain

Disease:

  • Neurological Disease
  • Phantom
  • Psychological

QIPCM – Advanced Imaging Core Lab

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Machine Learning in Health Data Integration Platforms

Presenter:

Julia Publicover

Abstract:

The QIPCM imaging core lab was conceived for high-quality, auditable and secure image management for clinical trials. More and more, users are looking to leverage the platform for large volume image analysis and processing, including radiomics and ML applications. This talk gave an overview of the QIPCM data-sharing platform including tools, services and quality management program that ensure high-quality image collection, analysis and regulatory compliance. I also discussed current use-cases in radiomics and ML and future aims.

Disease:

  • Cancer

Other:

  • Data source/database

Deep learning for automatic prostate segmentation in 3D ultrasound

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Machine Learning in Prostate Cancer

Presenter:

Nathan Orlando

Abstract:

Nathan discussed the development and validation of a generalizable and efficient deep learning method for automatic prostate segmentation in 3D ultrasound, compared performance to state of the art algorithms and explored the impact of dataset size and diversity on segmentation performance.

Modality:

  • US

Organ:

  • Prostate

Disease:

  • Cancer

MRI in Prostate Cancer: Opportunities for AI

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Machine Learning in Prostate Cancer

Presenter:

Masoom Haider

Abstract:

MRI has established itself as an adjunctive test in detecting occult prostate cancer. New guidelines will recommend MRI for patients with elevated PSA and no prior biopsy resulting in a marked increase in MRI in the province. There are several use cases that can benefit from the application of machine learning to improve diagnostic performance and outcomes, which was reviewed.

Modality:

  • MRI
  • Gene Expression

Organ:

  • Prostate

Disease:

  • Cancer

Machine learning for prognostic modelling in head and neck cancer using multimodal data

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Machine Learning in Head and Neck Cancer

Presenter:

Michal Kazmierski

Abstract:

Michal discussed the results of a machine learning challenge for head and neck cancer (HNC) survival prediction with the aim of 1) developing an accurate prognostic model for HNC survival using clinical, demographic and routinely collected CT imaging data and 2) evaluating the true added value of CT radiomics compared to other prognostic factors.

Modality:

  • CT

Organ:

  • Head and Neck

Disease:

  • Cancer

Radiomics and Machine Learning for Oropharyngeal Cancer

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Machine Learning in Head and Neck Cancer

Presenter:

Sarah Mattonen

Abstract:

Sarah discussed preliminary work investigating radiomics and machine learning models to predict outcomes and toxicities in patients with oropharyngeal cancer treated with chemoradiotherapy. Sarah also discussed efforts to validate existing models, and described some challenges and opportunities for radiomics research in head and neck cancer.

Modality:

  • CT
  • MR

Organ:

  • Head and Neck

Disease:

  • Cancer
  • All
  • - CT
  • - Gene Expression
  • - Genomics
  • - Histopathology
  • - MR
  • - Optical / Microscopy
  • - US

AI-Driven Multimodal Computational Pathology

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Computational Pathology and Cancer Prediction

Presenter:

Faisal Mahmood

Abstract:

This talk will discuss the use of AI in pathology and methods of training digitized images through the utilization of smaller patches to make the data efficient. Models have been developed to identify the most important images patches. In addition, the development of a breast cancer lymph node metastasis model will be discussed, showing how different imaging approaches were used. Models predicting histology have been developed for cancer applications.

Modality:

  • CT
  • Optical/microscopy
  • Histopathology
  • Genomics

Organ

  • Breast
  • Cardiac
  • Prostate

Disease

  • Cancer

AI-Based Precision Pathology

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Computational Pathology and Cancer Prediction

Presenter:

Mattias Rantalainen

Abstract:

This talk will discuss precision medicine and its use in the development of AI in histopathology for the development of outcome prediction and treatment response prediction models. The development of a risk stratification model for breast cancer histological assessment will be discussed and the development of a medical device.

Modality:

  • Optical/microscopy
  • Gene expression
  • Histopathology

Organ

  • Breast
  • Lung
  • Prostate
  • Skin
  • Colorectal

Disease

  • Cancer

Other

  • Commercialization

Machine learning to predict disease progression

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Predicting Disease Progression

Presenter:

Sun-In Lee

Abstract:

This talk will describe the use of text and phenotypic data in medical records for predicting patients’ clinical course, and potential uses for medical management. The presentation will be followed by questions and answers about the talk, and discussion focused on lessons learned, opportunities to collaborate, and sharing of data.

Modality:

  • Gene expression

Organ

  • Brain
  • Hematopathology
  • Lung
  • Skin

Disease

  • Cancer
  • Kidney and Liver Disease
  • Neurological Disease
  • Stroke and Cardiovascular
  • COVID

Other

  • Therapeutics
  • Personalized medicine

Artificial intelligence augmented ultrasound detection of hip dysplasia

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on AI and Precision Health Initiatives at the University of Alberta

Presenter:

Jacob Jaremko

Abstract:

These talks will present new efforts underway at the University of Alberta in Edmonton AB to develop and translate AI technology into clinical practice. The College of Health Sciences at UAlberta is leading a new initiative in AI for precision health. These efforts are conducted in collaboration with the Alberta Machine Intelligence Institute (Amii), one of three national AI institutes supported through the Pan-Canadian AI Strategy. Amii is located in Edmonton and supports 40 professors conducting basic and applied AI research. Precision Health is the largest application area within Amii.

The UAlberta College of Health Sciences also collaborates extensively with Alberta Health Services (AHS), Canada’s first and largest province-wide, fully integrated health system. AHS provides health services to over 4.4 million people. For the past 20 years they have developed and maintained a broadly integrated data warehouse to collect and protect health data everywhere they provide services. This allows construction of large population-level datasets for precision health research.

These presentations will discuss some of the projects currently underway, and look ahead at new efforts to expand AI and Precision Health research at UAlberta and beyond.

Modality:

  • US

Organ:

  • Musculoskeletal

Disease

  • Musculoskeletal Disease

Other

  • Commercialization

Sensitivity of convolutional neural networks to common imaging parameters, perturbations and artifacts in MRI

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Alzheimer’s and Small Vessel Disease Imaging

Presenter:

Lyndon Boone

Abstract:

A number of studies have shown that deep learning methods are capable of achieving near-human-level performance on neuroimaging segmentation tasks. With that said, most of the results quoted in the literature supporting this statement are in the context of test sets drawn from the same overarching dataset as the training data. Clinically-deployed models faced with out-of-distribution data (i.e. data that does not resemble the training set) may severely underperform relative to the standards set in the literature if they aren’t designed specifically with robustness to out-of-distribution data in mind. In this talk, Lyndon highlighted the sensitivity of modern CNN-based architectures to image corruptions, artifacts, and post-processing transforms commonly found in MRI. He then presented a methodology for benchmarking different architectures and models on the basis of robustness to out-of-distribution data, inspired by similar work in the computer vision literature.

Modality:

  • MR

Organ:

  • Brain

Deep Learning for Automated Segmentation of Left Ventricle Myocardium and Myocardial Scar From 3-D MR Images

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Machine Learning in Cardiac Imaging

Presenter:

Fatemeh Zabihollahy

Abstract:

Deep learning has demonstrated promise for various cardiac imaging applications. However, the performance is usually degraded when the models are trained with small and under-annotated training datasets and tested on previously unseen domains, limiting the potential for broad clinical use. In this talk, Fumin presented his recent work on combining deep learning and machine learning models for cardiac MRI segmentation, where smaller datasets and fewer annotations are required for algorithm training. He also provided examples of integrating the segmentation tools for myocardial infarct heterogeneity quantification in contrast enhancement MRI in the context of MRI-guided cardiac arrhythmia treatment.

Modality:

  • MR

Organ:

  • Cardiac

Disease

  • Heart Disease
  • Stroke and Cardiovascular

Deep learning with uncertainty quantification in MRI-guided radiation therapy

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Reproducibility and Good Practices in Machine Learning Research

Presenter:

Matt Hemsley

Abstract:

Deep learning methods are able to match or surpass the performance of ​​classical methods as well as human experts in a growing number of medical imaging related tasks. However, most deep learning methods are unable to quantify uncertainty, hindering clinical translation. In this talk, Matt presented an overview of techniques used to model uncertainty on network output, demonstrated the connection between uncertainty quantification and reproducibility, and presented examples where uncertainty estimates were obtained from networks that performed clinical tasks related to real-time MRI-guided adaptive radiation therapy.

Modality:

  • CT
  • MR

Organ:

  • Brain
  • Head and Neck

Disease

  • Cancer

Brain-CODE

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Machine Learning in Health Data Integration Platforms

Presenters:

Tom Mikkelsen, Kirk Nylen, Brendan Behan

Abstract:

The Ontario Brain Institute (OBI) provided an overview of our Brain-CODE platform, its data holdings, and the opportunity to access and analyze highly standardized data on people living with various brain disorders – ranging from pediatric neurodevelopmental disorders through to neurodegenerative diseases of aging. We hoped to engage high-end data scientists like you in a discussion around potential collaboration opportunities with our large network of clinical experts/researchers.\

Modality:

  • MR
  • Gene Expression

Organ:

  • Brain

Disease:

  • Neurological Disease
  • Phantom
  • Psychological

Deep learning for automatic prostate segmentation in 3D ultrasound

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Machine Learning in Prostate Cancer

Presenter:

Nathan Orlando

Abstract:

Nathan discussed the development and validation of a generalizable and efficient deep learning method for automatic prostate segmentation in 3D ultrasound, compared performance to state of the art algorithms and explored the impact of dataset size and diversity on segmentation performance.

Modality:

  • US

Organ:

  • Prostate

Disease:

  • Cancer

MRI in Prostate Cancer: Opportunities for AI

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Machine Learning in Prostate Cancer

Presenter:

Masoom Haider

Abstract:

MRI has established itself as an adjunctive test in detecting occult prostate cancer. New guidelines will recommend MRI for patients with elevated PSA and no prior biopsy resulting in a marked increase in MRI in the province. There are several use cases that can benefit from the application of machine learning to improve diagnostic performance and outcomes, which was reviewed.

Modality:

  • MRI
  • Gene Expression

Organ:

  • Prostate

Disease:

  • Cancer

Machine learning for prognostic modelling in head and neck cancer using multimodal data

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Machine Learning in Head and Neck Cancer

Presenter:

Michal Kazmierski

Abstract:

Michal discussed the results of a machine learning challenge for head and neck cancer (HNC) survival prediction with the aim of 1) developing an accurate prognostic model for HNC survival using clinical, demographic and routinely collected CT imaging data and 2) evaluating the true added value of CT radiomics compared to other prognostic factors.

Modality:

  • CT

Organ:

  • Head and Neck

Disease:

  • Cancer

Radiomics and Machine Learning for Oropharyngeal Cancer

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Machine Learning in Head and Neck Cancer

Presenter:

Sarah Mattonen

Abstract:

Sarah discussed preliminary work investigating radiomics and machine learning models to predict outcomes and toxicities in patients with oropharyngeal cancer treated with chemoradiotherapy. Sarah also discussed efforts to validate existing models, and described some challenges and opportunities for radiomics research in head and neck cancer.

Modality:

  • CT
  • MR

Organ:

  • Head and Neck

Disease:

  • Cancer
  • All
  • - Brain
  • - Breast
  • - Cardiac
  • - Colorectal
  • - Head-Neck
  • - Hematopathology
  • - Lung
  • - Musculoskeletal
  • - Prostate
  • - Skin

AI-Driven Multimodal Computational Pathology

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Computational Pathology and Cancer Prediction

Presenter:

Faisal Mahmood

Abstract:

This talk will discuss the use of AI in pathology and methods of training digitized images through the utilization of smaller patches to make the data efficient. Models have been developed to identify the most important images patches. In addition, the development of a breast cancer lymph node metastasis model will be discussed, showing how different imaging approaches were used. Models predicting histology have been developed for cancer applications.

Modality:

  • CT
  • Optical/microscopy
  • Histopathology
  • Genomics

Organ

  • Breast
  • Cardiac
  • Prostate

Disease

  • Cancer

AI-Based Precision Pathology

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Computational Pathology and Cancer Prediction

Presenter:

Mattias Rantalainen

Abstract:

This talk will discuss precision medicine and its use in the development of AI in histopathology for the development of outcome prediction and treatment response prediction models. The development of a risk stratification model for breast cancer histological assessment will be discussed and the development of a medical device.

Modality:

  • Optical/microscopy
  • Gene expression
  • Histopathology

Organ

  • Breast
  • Lung
  • Prostate
  • Skin
  • Colorectal

Disease

  • Cancer

Other

  • Commercialization

Machine learning to predict disease progression

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Predicting Disease Progression

Presenter:

Sun-In Lee

Abstract:

This talk will describe the use of text and phenotypic data in medical records for predicting patients’ clinical course, and potential uses for medical management. The presentation will be followed by questions and answers about the talk, and discussion focused on lessons learned, opportunities to collaborate, and sharing of data.

Modality:

  • Gene expression

Organ

  • Brain
  • Hematopathology
  • Lung
  • Skin

Disease

  • Cancer
  • Kidney and Liver Disease
  • Neurological Disease
  • Stroke and Cardiovascular
  • COVID

Other

  • Therapeutics
  • Personalized medicine

Artificial intelligence augmented ultrasound detection of hip dysplasia

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on AI and Precision Health Initiatives at the University of Alberta

Presenter:

Jacob Jaremko

Abstract:

These talks will present new efforts underway at the University of Alberta in Edmonton AB to develop and translate AI technology into clinical practice. The College of Health Sciences at UAlberta is leading a new initiative in AI for precision health. These efforts are conducted in collaboration with the Alberta Machine Intelligence Institute (Amii), one of three national AI institutes supported through the Pan-Canadian AI Strategy. Amii is located in Edmonton and supports 40 professors conducting basic and applied AI research. Precision Health is the largest application area within Amii.

The UAlberta College of Health Sciences also collaborates extensively with Alberta Health Services (AHS), Canada’s first and largest province-wide, fully integrated health system. AHS provides health services to over 4.4 million people. For the past 20 years they have developed and maintained a broadly integrated data warehouse to collect and protect health data everywhere they provide services. This allows construction of large population-level datasets for precision health research.

These presentations will discuss some of the projects currently underway, and look ahead at new efforts to expand AI and Precision Health research at UAlberta and beyond.

Modality:

  • US

Organ:

  • Musculoskeletal

Disease

  • Musculoskeletal Disease

Other

  • Commercialization

Sensitivity of convolutional neural networks to common imaging parameters, perturbations and artifacts in MRI

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Alzheimer’s and Small Vessel Disease Imaging

Presenter:

Lyndon Boone

Abstract:

A number of studies have shown that deep learning methods are capable of achieving near-human-level performance on neuroimaging segmentation tasks. With that said, most of the results quoted in the literature supporting this statement are in the context of test sets drawn from the same overarching dataset as the training data. Clinically-deployed models faced with out-of-distribution data (i.e. data that does not resemble the training set) may severely underperform relative to the standards set in the literature if they aren’t designed specifically with robustness to out-of-distribution data in mind. In this talk, Lyndon highlighted the sensitivity of modern CNN-based architectures to image corruptions, artifacts, and post-processing transforms commonly found in MRI. He then presented a methodology for benchmarking different architectures and models on the basis of robustness to out-of-distribution data, inspired by similar work in the computer vision literature.

Modality:

  • MR

Organ:

  • Brain

Deep Learning for Automated Segmentation of Left Ventricle Myocardium and Myocardial Scar From 3-D MR Images

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Machine Learning in Cardiac Imaging

Presenter:

Fatemeh Zabihollahy

Abstract:

Deep learning has demonstrated promise for various cardiac imaging applications. However, the performance is usually degraded when the models are trained with small and under-annotated training datasets and tested on previously unseen domains, limiting the potential for broad clinical use. In this talk, Fumin presented his recent work on combining deep learning and machine learning models for cardiac MRI segmentation, where smaller datasets and fewer annotations are required for algorithm training. He also provided examples of integrating the segmentation tools for myocardial infarct heterogeneity quantification in contrast enhancement MRI in the context of MRI-guided cardiac arrhythmia treatment.

Modality:

  • MR

Organ:

  • Cardiac

Disease

  • Heart Disease
  • Stroke and Cardiovascular

Deep learning with uncertainty quantification in MRI-guided radiation therapy

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Reproducibility and Good Practices in Machine Learning Research

Presenter:

Matt Hemsley

Abstract:

Deep learning methods are able to match or surpass the performance of ​​classical methods as well as human experts in a growing number of medical imaging related tasks. However, most deep learning methods are unable to quantify uncertainty, hindering clinical translation. In this talk, Matt presented an overview of techniques used to model uncertainty on network output, demonstrated the connection between uncertainty quantification and reproducibility, and presented examples where uncertainty estimates were obtained from networks that performed clinical tasks related to real-time MRI-guided adaptive radiation therapy.

Modality:

  • CT
  • MR

Organ:

  • Brain
  • Head and Neck

Disease

  • Cancer

Brain-CODE

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Machine Learning in Health Data Integration Platforms

Presenters:

Tom Mikkelsen, Kirk Nylen, Brendan Behan

Abstract:

The Ontario Brain Institute (OBI) provided an overview of our Brain-CODE platform, its data holdings, and the opportunity to access and analyze highly standardized data on people living with various brain disorders – ranging from pediatric neurodevelopmental disorders through to neurodegenerative diseases of aging. We hoped to engage high-end data scientists like you in a discussion around potential collaboration opportunities with our large network of clinical experts/researchers.\

Modality:

  • MR
  • Gene Expression

Organ:

  • Brain

Disease:

  • Neurological Disease
  • Phantom
  • Psychological

Deep learning for automatic prostate segmentation in 3D ultrasound

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Machine Learning in Prostate Cancer

Presenter:

Nathan Orlando

Abstract:

Nathan discussed the development and validation of a generalizable and efficient deep learning method for automatic prostate segmentation in 3D ultrasound, compared performance to state of the art algorithms and explored the impact of dataset size and diversity on segmentation performance.

Modality:

  • US

Organ:

  • Prostate

Disease:

  • Cancer

MRI in Prostate Cancer: Opportunities for AI

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Machine Learning in Prostate Cancer

Presenter:

Masoom Haider

Abstract:

MRI has established itself as an adjunctive test in detecting occult prostate cancer. New guidelines will recommend MRI for patients with elevated PSA and no prior biopsy resulting in a marked increase in MRI in the province. There are several use cases that can benefit from the application of machine learning to improve diagnostic performance and outcomes, which was reviewed.

Modality:

  • MRI
  • Gene Expression

Organ:

  • Prostate

Disease:

  • Cancer

Machine learning for prognostic modelling in head and neck cancer using multimodal data

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Machine Learning in Head and Neck Cancer

Presenter:

Michal Kazmierski

Abstract:

Michal discussed the results of a machine learning challenge for head and neck cancer (HNC) survival prediction with the aim of 1) developing an accurate prognostic model for HNC survival using clinical, demographic and routinely collected CT imaging data and 2) evaluating the true added value of CT radiomics compared to other prognostic factors.

Modality:

  • CT

Organ:

  • Head and Neck

Disease:

  • Cancer

Radiomics and Machine Learning for Oropharyngeal Cancer

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Machine Learning in Head and Neck Cancer

Presenter:

Sarah Mattonen

Abstract:

Sarah discussed preliminary work investigating radiomics and machine learning models to predict outcomes and toxicities in patients with oropharyngeal cancer treated with chemoradiotherapy. Sarah also discussed efforts to validate existing models, and described some challenges and opportunities for radiomics research in head and neck cancer.

Modality:

  • CT
  • MR

Organ:

  • Head and Neck

Disease:

  • Cancer
  • All
  • - COVID
  • - Cancer
  • - Heart Disease
  • - Kidney and Liver Disease
  • - Musculoskeletal Disease
  • - Neurological Disease
  • - Phantom
  • - Psychological
  • - Stroke and Cardiovascular

AI-Driven Multimodal Computational Pathology

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Computational Pathology and Cancer Prediction

Presenter:

Faisal Mahmood

Abstract:

This talk will discuss the use of AI in pathology and methods of training digitized images through the utilization of smaller patches to make the data efficient. Models have been developed to identify the most important images patches. In addition, the development of a breast cancer lymph node metastasis model will be discussed, showing how different imaging approaches were used. Models predicting histology have been developed for cancer applications.

Modality:

  • CT
  • Optical/microscopy
  • Histopathology
  • Genomics

Organ

  • Breast
  • Cardiac
  • Prostate

Disease

  • Cancer

AI-Based Precision Pathology

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Computational Pathology and Cancer Prediction

Presenter:

Mattias Rantalainen

Abstract:

This talk will discuss precision medicine and its use in the development of AI in histopathology for the development of outcome prediction and treatment response prediction models. The development of a risk stratification model for breast cancer histological assessment will be discussed and the development of a medical device.

Modality:

  • Optical/microscopy
  • Gene expression
  • Histopathology

Organ

  • Breast
  • Lung
  • Prostate
  • Skin
  • Colorectal

Disease

  • Cancer

Other

  • Commercialization

Machine learning to predict disease progression

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Predicting Disease Progression

Presenter:

Sun-In Lee

Abstract:

This talk will describe the use of text and phenotypic data in medical records for predicting patients’ clinical course, and potential uses for medical management. The presentation will be followed by questions and answers about the talk, and discussion focused on lessons learned, opportunities to collaborate, and sharing of data.

Modality:

  • Gene expression

Organ

  • Brain
  • Hematopathology
  • Lung
  • Skin

Disease

  • Cancer
  • Kidney and Liver Disease
  • Neurological Disease
  • Stroke and Cardiovascular
  • COVID

Other

  • Therapeutics
  • Personalized medicine

Segment Anything in Medical Images

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Enforcing Geometry in Machine Learning for Computational Neuroimaging

Presenter:

Jun Ma

Abstract:

Medical imaging plays an indispensable role in clinical practice. Accurate and efficient medical image segmentation provides a means of delineating regions of interest and quantifying various clinical metrics. However, building customized segmentation models for each medical imaging task can be a daunting and time-consuming process, limiting the widespread adoption in clinical practice. In this talk, Jun will introduce MedSAM, a segmentation foundation model that enables universal segmentation across a wide range of medical imaging tasks and modalities. MedSAM achieved remarkable improvements in 30 segmentation tasks, surpassing the existing segmentation foundation model by a large margin. MedSAM also demonstrated zero-shot and few-shot capabilities to segment unseen tumor types and adapt to new imaging modalities with minimal effort. The results validate the versatility of MedSAM compared to existing customized segmentation models, emphasizing its potential to transform medical image segmentation and enhance clinical practice.

Disease

  • Cancer

Other

  • Resource Sharing
  • Reproducibility

Artificial intelligence augmented ultrasound detection of hip dysplasia

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on AI and Precision Health Initiatives at the University of Alberta

Presenter:

Jacob Jaremko

Abstract:

These talks will present new efforts underway at the University of Alberta in Edmonton AB to develop and translate AI technology into clinical practice. The College of Health Sciences at UAlberta is leading a new initiative in AI for precision health. These efforts are conducted in collaboration with the Alberta Machine Intelligence Institute (Amii), one of three national AI institutes supported through the Pan-Canadian AI Strategy. Amii is located in Edmonton and supports 40 professors conducting basic and applied AI research. Precision Health is the largest application area within Amii.

The UAlberta College of Health Sciences also collaborates extensively with Alberta Health Services (AHS), Canada’s first and largest province-wide, fully integrated health system. AHS provides health services to over 4.4 million people. For the past 20 years they have developed and maintained a broadly integrated data warehouse to collect and protect health data everywhere they provide services. This allows construction of large population-level datasets for precision health research.

These presentations will discuss some of the projects currently underway, and look ahead at new efforts to expand AI and Precision Health research at UAlberta and beyond.

Modality:

  • US

Organ:

  • Musculoskeletal

Disease

  • Musculoskeletal Disease

Other

  • Commercialization

Deep Learning for Automated Segmentation of Left Ventricle Myocardium and Myocardial Scar From 3-D MR Images

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Machine Learning in Cardiac Imaging

Presenter:

Fatemeh Zabihollahy

Abstract:

Deep learning has demonstrated promise for various cardiac imaging applications. However, the performance is usually degraded when the models are trained with small and under-annotated training datasets and tested on previously unseen domains, limiting the potential for broad clinical use. In this talk, Fumin presented his recent work on combining deep learning and machine learning models for cardiac MRI segmentation, where smaller datasets and fewer annotations are required for algorithm training. He also provided examples of integrating the segmentation tools for myocardial infarct heterogeneity quantification in contrast enhancement MRI in the context of MRI-guided cardiac arrhythmia treatment.

Modality:

  • MR

Organ:

  • Cardiac

Disease

  • Heart Disease
  • Stroke and Cardiovascular

Deep learning with uncertainty quantification in MRI-guided radiation therapy

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Reproducibility and Good Practices in Machine Learning Research

Presenter:

Matt Hemsley

Abstract:

Deep learning methods are able to match or surpass the performance of ​​classical methods as well as human experts in a growing number of medical imaging related tasks. However, most deep learning methods are unable to quantify uncertainty, hindering clinical translation. In this talk, Matt presented an overview of techniques used to model uncertainty on network output, demonstrated the connection between uncertainty quantification and reproducibility, and presented examples where uncertainty estimates were obtained from networks that performed clinical tasks related to real-time MRI-guided adaptive radiation therapy.

Modality:

  • CT
  • MR

Organ:

  • Brain
  • Head and Neck

Disease

  • Cancer

Brain-CODE

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Machine Learning in Health Data Integration Platforms

Presenters:

Tom Mikkelsen, Kirk Nylen, Brendan Behan

Abstract:

The Ontario Brain Institute (OBI) provided an overview of our Brain-CODE platform, its data holdings, and the opportunity to access and analyze highly standardized data on people living with various brain disorders – ranging from pediatric neurodevelopmental disorders through to neurodegenerative diseases of aging. We hoped to engage high-end data scientists like you in a discussion around potential collaboration opportunities with our large network of clinical experts/researchers.\

Modality:

  • MR
  • Gene Expression

Organ:

  • Brain

Disease:

  • Neurological Disease
  • Phantom
  • Psychological

QIPCM – Advanced Imaging Core Lab

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Machine Learning in Health Data Integration Platforms

Presenter:

Julia Publicover

Abstract:

The QIPCM imaging core lab was conceived for high-quality, auditable and secure image management for clinical trials. More and more, users are looking to leverage the platform for large volume image analysis and processing, including radiomics and ML applications. This talk gave an overview of the QIPCM data-sharing platform including tools, services and quality management program that ensure high-quality image collection, analysis and regulatory compliance. I also discussed current use-cases in radiomics and ML and future aims.

Disease:

  • Cancer

Other:

  • Data source/database

Deep learning for automatic prostate segmentation in 3D ultrasound

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Machine Learning in Prostate Cancer

Presenter:

Nathan Orlando

Abstract:

Nathan discussed the development and validation of a generalizable and efficient deep learning method for automatic prostate segmentation in 3D ultrasound, compared performance to state of the art algorithms and explored the impact of dataset size and diversity on segmentation performance.

Modality:

  • US

Organ:

  • Prostate

Disease:

  • Cancer

MRI in Prostate Cancer: Opportunities for AI

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Machine Learning in Prostate Cancer

Presenter:

Masoom Haider

Abstract:

MRI has established itself as an adjunctive test in detecting occult prostate cancer. New guidelines will recommend MRI for patients with elevated PSA and no prior biopsy resulting in a marked increase in MRI in the province. There are several use cases that can benefit from the application of machine learning to improve diagnostic performance and outcomes, which was reviewed.

Modality:

  • MRI
  • Gene Expression

Organ:

  • Prostate

Disease:

  • Cancer

Machine learning for prognostic modelling in head and neck cancer using multimodal data

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Machine Learning in Head and Neck Cancer

Presenter:

Michal Kazmierski

Abstract:

Michal discussed the results of a machine learning challenge for head and neck cancer (HNC) survival prediction with the aim of 1) developing an accurate prognostic model for HNC survival using clinical, demographic and routinely collected CT imaging data and 2) evaluating the true added value of CT radiomics compared to other prognostic factors.

Modality:

  • CT

Organ:

  • Head and Neck

Disease:

  • Cancer

Radiomics and Machine Learning for Oropharyngeal Cancer

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Machine Learning in Head and Neck Cancer

Presenter:

Sarah Mattonen

Abstract:

Sarah discussed preliminary work investigating radiomics and machine learning models to predict outcomes and toxicities in patients with oropharyngeal cancer treated with chemoradiotherapy. Sarah also discussed efforts to validate existing models, and described some challenges and opportunities for radiomics research in head and neck cancer.

Modality:

  • CT
  • MR

Organ:

  • Head and Neck

Disease:

  • Cancer
  • All
  • - Commercialization
  • - Data Source / Database
  • - Personalized Medicine
  • - Reproducibility
  • - Resource Sharing
  • - Therapeutics

AI-Based Precision Pathology

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Computational Pathology and Cancer Prediction

Presenter:

Mattias Rantalainen

Abstract:

This talk will discuss precision medicine and its use in the development of AI in histopathology for the development of outcome prediction and treatment response prediction models. The development of a risk stratification model for breast cancer histological assessment will be discussed and the development of a medical device.

Modality:

  • Optical/microscopy
  • Gene expression
  • Histopathology

Organ

  • Breast
  • Lung
  • Prostate
  • Skin
  • Colorectal

Disease

  • Cancer

Other

  • Commercialization

Machine learning to predict disease progression

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Predicting Disease Progression

Presenter:

Sun-In Lee

Abstract:

This talk will describe the use of text and phenotypic data in medical records for predicting patients’ clinical course, and potential uses for medical management. The presentation will be followed by questions and answers about the talk, and discussion focused on lessons learned, opportunities to collaborate, and sharing of data.

Modality:

  • Gene expression

Organ

  • Brain
  • Hematopathology
  • Lung
  • Skin

Disease

  • Cancer
  • Kidney and Liver Disease
  • Neurological Disease
  • Stroke and Cardiovascular
  • COVID

Other

  • Therapeutics
  • Personalized medicine

Segment Anything in Medical Images

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Enforcing Geometry in Machine Learning for Computational Neuroimaging

Presenter:

Jun Ma

Abstract:

Medical imaging plays an indispensable role in clinical practice. Accurate and efficient medical image segmentation provides a means of delineating regions of interest and quantifying various clinical metrics. However, building customized segmentation models for each medical imaging task can be a daunting and time-consuming process, limiting the widespread adoption in clinical practice. In this talk, Jun will introduce MedSAM, a segmentation foundation model that enables universal segmentation across a wide range of medical imaging tasks and modalities. MedSAM achieved remarkable improvements in 30 segmentation tasks, surpassing the existing segmentation foundation model by a large margin. MedSAM also demonstrated zero-shot and few-shot capabilities to segment unseen tumor types and adapt to new imaging modalities with minimal effort. The results validate the versatility of MedSAM compared to existing customized segmentation models, emphasizing its potential to transform medical image segmentation and enhance clinical practice.

Disease

  • Cancer

Other

  • Resource Sharing
  • Reproducibility

Artificial intelligence augmented ultrasound detection of hip dysplasia

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on AI and Precision Health Initiatives at the University of Alberta

Presenter:

Jacob Jaremko

Abstract:

These talks will present new efforts underway at the University of Alberta in Edmonton AB to develop and translate AI technology into clinical practice. The College of Health Sciences at UAlberta is leading a new initiative in AI for precision health. These efforts are conducted in collaboration with the Alberta Machine Intelligence Institute (Amii), one of three national AI institutes supported through the Pan-Canadian AI Strategy. Amii is located in Edmonton and supports 40 professors conducting basic and applied AI research. Precision Health is the largest application area within Amii.

The UAlberta College of Health Sciences also collaborates extensively with Alberta Health Services (AHS), Canada’s first and largest province-wide, fully integrated health system. AHS provides health services to over 4.4 million people. For the past 20 years they have developed and maintained a broadly integrated data warehouse to collect and protect health data everywhere they provide services. This allows construction of large population-level datasets for precision health research.

These presentations will discuss some of the projects currently underway, and look ahead at new efforts to expand AI and Precision Health research at UAlberta and beyond.

Modality:

  • US

Organ:

  • Musculoskeletal

Disease

  • Musculoskeletal Disease

Other

  • Commercialization

Design, conduct, and reporting of radiomic analyses: Let’s not reinvent the wheel

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Reproducibility and Good Practices in Machine Learning Research

Presenter:

Chaya Moskowitz

Abstract:

The number of published papers reporting on radiomic analyses has been growing exponentially. Very few of these paper produce results that are translated into clinical practice at least in part because of methodological flaws in the work. Although complex methods for producing radiomic signatures are increasingly available and user-friendly and progress has been made in radiomic biomarker taxonomy and standardization, fundamental elements of study design, rigorous statistical analysis, and quality of reporting methods and results are frequently overlooked. In this talk, Chaya highlighted common pitfalls encountered in radiomic studies that could be avoided by knowledge of existing methods and adherence to existing reporting standards.

Other:

  • Reproducibility

Improving Peproducibility in Machine Learning Research

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Reproducibility and Good Practices in Machine Learning Research

Presenter:

Benjamin Haibe-Kains

Abstract:

While biostatistics and machine learning are essential to analyze biomedical data, researchers are facing multiple challenges around research reproducibility and transparency. It is essential for independent researchers to be able to scrutinize and reproduce the results of a study using its materials, and build upon them in future studies.

Computational reproducibility is achievable when the data can easily be shared and the required computational resources are relatively common. However, the complexity of current algorithms and their implementation, the need for specific computer hardware and the use of sensitive biomedical data represent major obstacles in healthy-related research. In this talk, Benjamin described the various aspects of a typical biomedical study that are necessary for reproducibility and the platforms that exist for sharing these materials with the scientific community.

Other:

  • Resource Sharing
  • Reproducibility

Introduction to the Ontario Health Data Platform

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Machine Learning in Health Data Integration Platforms

Presenter:

Amber Simpson

Abstract:

The Ontario Health Data Platform (OHDP) is a secure, federated high-performance computing environment built for linkage and analysis of large health data sets. The platform was built to aid Ontario in their fight against COVID-19. This talk described the platform, available data, and the potential for future data such as genomic and imaging data repositories.

Other:

  • Data source/database

Brain-CODE

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Machine Learning in Health Data Integration Platforms

Presenters:

Tom Mikkelsen, Kirk Nylen, Brendan Behan

Abstract:

The Ontario Brain Institute (OBI) provided an overview of our Brain-CODE platform, its data holdings, and the opportunity to access and analyze highly standardized data on people living with various brain disorders – ranging from pediatric neurodevelopmental disorders through to neurodegenerative diseases of aging. We hoped to engage high-end data scientists like you in a discussion around potential collaboration opportunities with our large network of clinical experts/researchers.\

Modality:

  • MR
  • Gene Expression

Organ:

  • Brain

Disease:

  • Neurological Disease
  • Phantom
  • Psychological

QIPCM – Advanced Imaging Core Lab

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Open Forum on Machine Learning in Health Data Integration Platforms

Presenter:

Julia Publicover

Abstract:

The QIPCM imaging core lab was conceived for high-quality, auditable and secure image management for clinical trials. More and more, users are looking to leverage the platform for large volume image analysis and processing, including radiomics and ML applications. This talk gave an overview of the QIPCM data-sharing platform including tools, services and quality management program that ensure high-quality image collection, analysis and regulatory compliance. I also discussed current use-cases in radiomics and ML and future aims.

Disease:

  • Cancer

Other:

  • Data source/database