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admmm

2022 | Loss odyssey in medical image segmentation

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Short Description: In this paper, we present a comprehensive review of segmentation loss functions in an organized manner. We also conduct the first large-scale analysis of 20 general loss functions on four typical 3D segmentation tasks involving six public datasets from 10+ medical centres. Our code and segmentation results are publicly available and can serve as a loss function benchmark. We hope this work will also provide insights on new loss function development for the community.
Modality: CT
Organ: Liver, Pancreas
Disease: Cancer

2021 | AMINN: Autoencoder-Based Multiple Instance Neural Network Improves Outcome Prediction in Multifocal Liver Metastases

 150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Short Description: We built a multiple instance learning network with radiomic features of liver MRI to predict survival of multifocal colorectal cancer liver metastases patients. We empirically validated our hypothesis that incorporating imaging features of all lesions improves outcome prediction for multifocal cancer. We released our code at https://github.com/martellab-sri/AMINN.
Modality: MR
Organ: Liver
Disease: Cancer

Machine learning for molecular and cellular imaging – Forum, April 22, 2022

 150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Hartland Jackson will talk about machine learning applied to imaging mass cytometry. Parvin Mousavi will present her work on mass spectrometry applied to real-time intraoperative tumour margin detection. David Andrews will talk about machine learning in high throughput single-cell imaging. The talks will be followed by discussion focused on lessons learned, opportunities to collaborate and sharing of resources.

MaLMIC sponsored machine learning session at ImNO 2022 – Forum, March 23, 2022

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MaLMIC is organizing a machine learning session at ImNO 2022 on Wednesday, March 23, 2022. Alison Noble, a research scientist at University of Oxford, will give a keynote presentation followed by oral and pitch presentations focused on imaging machine learning applications.

Federated learning – Forum, February 18, 2022

 150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Kam Kafi, Director of Clinical Strategy and Oncology at Imagia will outline the case for federated learning and work underway between Imagia and collaborators. Spyridon Bakas, Assistant Professor at UPenn, will talk about an open platform he has developed to support federated learning for radiology. Aline Talhouk, Assistant Professor at UBC, will talk about privacy issues and introduce the LEAP platform. The presentations will be followed by group discussion on lessons learned, opportunities to collaborate, and sharing of resources.

Machine learning in image-guided intervention – Forum, January 21, 2022

 150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Derek Cool, an interventional radiologist, discussed the clinical need for machine learning in image-guided intervention. Purang Abolmaesumi and Derek Gillies then presented their research work in the area. The talks were followed by discussion focused on lessons learned, opportunities to collaborate and sharing of resources.

Breast Cancer Machine Learning – Forum, November 19, 2021

 150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Dr. Emily Conant, a radiologist from the University of Pennsylvania, gave the lead-off talk and presented the clinical problems and opportunities where machine learning could make significant impact. She was followed by Professor Nico Karssemeijer from Radboud University in the Netherlands. He has developed important algorithms for breast density and is implementing AI approaches to detection of breast cancer. Grey Kuling, PhD candidate from Sunnybrook Research Institute gave the third talk covering the more technical approach of their research work. The talks were followed by discussion focused on lessons learned, opportunities to collaborate and sharing of resources.

Lessons Learned in Genomics and Imaging Machine Learning – Forum, October 15, 2021

 150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Phedias Diamandis, a neuropathologist and researcher at UHN, discussed the clinical challenges of classifying brain malignancies and the promise of combining ‘omics with digital morphology to help untangle the substantial heterogeneity in these tumours. This was followed by a talk by Wail Ba-Alawi, an affiliate scientist at Princess Margaret Cancer Centre, on using multi-omics techniques for cancer biomarker discovery, and by Anglin Dent, an MSc student at University of Toronto, who expanded on Phedias’ talk by discussing progress in applying deep learning approaches to define biologically distinct subpopulations in glioblastoma. The talks were followed by discussion focused on lessons learned, opportunities to collaborate and sharing of resources.

Immunotherapy Imaging – Forum, September 17, 2021

 150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Annette Hay, a hematologist and clinician-scientist, talked about the ExCELLirate platform followed by two researchers, Tricia Cottrell and Alison Cheung, described their immunotherapy imaging machine learning research. This was followed by discussion focused on lessons learned, opportunities to collaborate and sharing of resources.

Alzheimer’s and Small Vessel Disease Imaging – Forum, July 16, 2021

 150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Sandra Black, a neurologist, talked about the clinical perspective followed by two researchers, Maged Goubran and Lyndon Boone, describing their machine learning research in the area. This was followed by discussion focused on lessons learned, opportunities to collaborate and sharing of resources.