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MaLMIC sponsored machine learning session at ImNO 2023 – Symposium Session, March 23/24, 2023

1024 767 MaLMIC - Machine Learning in Medical Imaging Consortium

MaLMIC is organizing a machine learning session at the ImNO 2023 symposium in March 2023. Details about the session will be announced later this year.

Machine Learning in Radiology – Forum, July 15 2022

1000 563 MaLMIC - Machine Learning in Medical Imaging Consortium

Jaron Chong will start the forum with an overview of the state of AI for medical imaging in Canada. The second presentation will be by Alex Bilbily talking about the road to building impactful AI medical devices. The third presentation will be by Benjamin Fine on pre-deployment evaluation. The three talks will be followed by discussion focused on lessons learned, opportunities to collaborate and sharing of resources.

Career Opportunity in AI/Machine Learning

1024 576 MaLMIC - Machine Learning in Medical Imaging Consortium

Ontario Institute for Cancer Research-funded Scientist or Senior Scientist Position

Machine learning in Liquid Biopsy – Forum, June 17 2022

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Alex Wyatt will give a general introduction to liquid biopsy. This will be followed by Eric Zhao talking about his research work using liquid biopsy in head and neck cancer, and Nick Cheng talking about his research work using liquid biopsy for early detection of high-risk breast cancer. The presentations will be followed by questions and answers about the talks, and discussion focused on lessons learned, opportunities to collaborate and sharing of resources.

Machine Learning in Vascular Imaging– Forum, May 27, 2022

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

David Spence, a clinician, will talk about the need for machine learning applications in vascular imaging. His presentation will be followed by two researchers, Eran Ukwatta and Fumin Guo, talking about their work in vascular imaging machine learning. The talks will be followed by a discussion focused on lessons learned, opportunities to collaborate and sharing of resources.

Canada Excellence Research Chair (CERC) in Medical Physics

1024 683 MaLMIC - Machine Learning in Medical Imaging Consortium

Ryerson University invites applications to the Canada Excellence Research Chairs (CERC) Program from a recognized leader.

2022 | Self-supervised driven consistency training for annotation efficient histopathology image analysis

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

In this work, we propose two novel strategies for unsupervised representation learning in histology: (i) a self-supervised pretext task that harnesses the underlying multi-resolution contextual cues in histology whole-slide images to learn a powerful supervisory signal for unsupervised representation learning; (ii) a new teacher-student semi-supervised consistency paradigm that learns to effectively transfer the pretrained representations to downstream tasks based on prediction consistency with the task-specific unlabeled data. We carry out extensive validation experiments on three histopathology benchmark datasets across two classification and one regression based tasks.

2022 | Self supervised contrastive learning for digital histopathology.

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

Short Description: We apply a contrastive self-supervised method to digital histopathology. A large-scale study with 57 histopathology datasets without labels was conducted. Our study focuses on differences between natural-scene and histopathology images. We find combining multiple multi-organ datasets improves task performances. Using more pretraining data has diminishing returns after around 50,000 images.
Modality: Optical/Microscopy
Organ: Prostate, Breast, Colorectal
Disease: Cancer

2022 | Loss odyssey in medical image segmentation

150 150 MaLMIC - Machine Learning in Medical Imaging Consortium

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