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admmm

Machine Learning in Image Reconstruction – Forum, January 20, 2023

 1024 576 MaLMIC - Machine Learning in Medical Imaging Consortium

For the January forum on machine learning in image reconstruction, the first presentation by Ge Wang, a researcher, will be about CT image reconstruction and the second presentation by Charlie Millard, a postdoctoral fellow, will be about MRI image reconstruction. The talks will be followed by discussion focused on lessons learned, opportunities to collaborate, and sharing of resources.

Machine Learning in SaRS-COV-2 and COVID Medical Imaging – Forum, November 18, 2022

 1024 389 MaLMIC - Machine Learning in Medical Imaging Consortium

Douglas Lee, a cardiologist and clinician scientist, will present his work on machine learning in COVID-related risk analysis using large-scale population data. Simon Graham, a senior scientist, will present his recent work on COVID-related medical imaging. The talks will be followed by a discussion focused on lessons learned, opportunities to collaborate, and sharing of resources.

Natural Language Processing – Forum, October 28, 2022

 1024 545 MaLMIC - Machine Learning in Medical Imaging Consortium

Richard Do, a radiologist, will start the forum with a presentation on natural language processing (NLP) of radiology reports to track metastatic spread. Heidi Hanson, a researcher, will talk about NLP in cancer surveillance. The two presentations will be followed by discussion focused on lessons learned, opportunities to collaborate, and sharing of resources.

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