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FULCRUM: A model for the design of educational tools and enabling machine learning/deep learning tools for automated lesion detection

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April 25, 2025 from 3:00 to 4:30 p.m. ET
Glenn Bauman and Katherine Zukotynski will talk at the April MaLMIC forum about FULCRUM, a clinical imaging database they created. It is a database of PSMA PET/CT scans acquired as part of a province-wide prospective study. It is composed of approximately 1000 men with recurrent prostate cancer imaged with 18F-DCFPyL or 18F-PSMA 1007 with centralized expert review, annotation and segmentation of PET-detected recurrent prostate cancer foci. The database is being used to design educational tools and enable machine learning/deep learning tools for automated lesion detection.

Join MaLMIC at the 2025 IGT x ImNO Joint Symposium

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MaLMIC is pleased to support the 2025 IGT x ImNO Joint Symposium in Toronto ON on March 5 and 6, 2025. Throughout the symposium, there will be talks, pitches and posters on AI, deep learning and machine learning.

The Open Health Imaging Foundation – Forum, January 31, 2025

1024 604 MaLMIC - Machine Learning in Medical Imaging Consortium

January 31, 2025
3:00 to 4:30 p.m. Eastern

Gordon Harris and Alireza Sedghi presented information about the Open Health Imaging Foundation (https://ohif.org/), an open source web based medical imaging framework, at the January forum. The presentations were followed by questions and answers about the talks, and discussions focused on lessons learned, opportunities to collaborate, and sharing of data.

Multi-Task Learning for Integrated Automated Contouring and Voxel-Based Dose Prediction in Radiotherapy

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Multi-Task Learning for Integrated Automated Contouring and Voxel-Based Dose Prediction in Radiotherapy Past Event Page Presenter: Sangwook Kim, PhD Student University of Toronto Abstract: Deep learning-based automated treatment planning cansignificantly…

Applications of Automated Treatment Planning in Adaptive Radiotherapy

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Applications of Automated Treatment Planning in Adaptive Radiotherapy Past Event Page Presenter: Aly Khalifa, PhD Student University of Toronto Abstract: Technological advancements in radiotherapy have significantly enhanced treatment precision and…

Chatbots as Mental Healthcare Proxies: Possibilities and Limitations

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Chatbots as Mental Healthcare Proxies: Possibilities and Limitations Past Event Page Presenter: Catherine Stinson, PhD Assistant Professor, Queen’s University Abstract: There is a great need for more affordable, accessible mental…

Healthcare Horizons: AI to Navigate the Evolving Landscape

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Healthcare Horizons: AI to Navigate the Evolving Landscape Past Event Page Presenter: Elham Dolatabadi, PhD Assistant Professor, York University Abstract: As healthcare evolves, data expands, and learning algorithms become increasingly…

Machine learning in radiotherapy applications – Forum, October 25, 2024

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October 25, 2024
3:00 to 4:30 p.m. Eastern

Aly Khalifa and Sanwook Kim will present at the MaLMIC fall forum, hosted by Julia Publicover. The presentations will be followed by questions and answers about the talks, and discussion focused on lessons learned, opportunities to collaborate, and sharing of data.

Using machine learning to address complex challenges in human health – Forum, September 20, 2024

1024 597 MaLMIC - Machine Learning in Medical Imaging Consortium

September 20, 2024
3:00 to 4:30 p.m. Eastern

Elham Dolatabadi and Catherine Stinson will present at the MaLMIC fall forum, hosted by Amber Simpson. The presentations wil be followed by questions and answers about the talks, and discussion focused on lessons learned, opportunities to collaborate, and sharing of data.

Enhancing cancer research with quantitative imaging: A deep dive into QIPCM at UHN – Webinar, June 27 2024

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Join us for an engaging webinar on June 27th at 11:30am that showcases the Quantitative Imaging for Personalized Cancer Medicine (QIPCM) program which provides end-to-end testing and analysis support for clinical trials to improve consistency and reliability in clinical trial imaging data.

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