MaLMIC - Machine Learning in Medical Imaging Consortium
Brain-CODE2021-03-19 Open Forum on Machine Learning in Health Data Integration Platforms Past Event Page Presenters: Tom Mikkelsen, Kirk Nylen, Brendan Behan Abstract: The Ontario Brain Institute (OBI) provided an overview…
QIPCM2021-03-19 Open Forum on Machine Learning in Health Data Integration Platforms Past Event Page Presenter: Julia Publicover Abstract: The QIPCM imaging core lab was conceived for high-quality, auditable and secure…
Orlando2021-02-19 Open Forum on Machine Learning in Prostate Cancer Past Event Page Presenter: Nathan Orlando Abstract: Nathan discussed the development and validation of a generalizable and efficient deep learning method…
OICR-Prostate-ML-Haider Open Forum on Machine Learning in Prostate Cancer Past Event Page Presenter: Masoom Haider Abstract: MRI has established itself as an adjunctive test in detecting occult prostate cancer. New…
Kazmierski2021-01-21 Open Forum on Machine Learning in Head and Neck Cancer Past Event Page Presenter: Michal Kazmierski Abstract: Michal discussed the results of a machine learning challenge for head and…
Mattonen2021-01-21 Open Forum on Machine Learning in Head and Neck Cancer Past Event Page Presenter: Sarah Mattonen Abstract: Sarah discussed preliminary work investigating radiomics and machine learning models to predict…
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MaLMIC is pleased to support the ImNO 2024 symposium in Mississauga ON on March 19 and 20, 2024. Here are some of the sessions featuring AI and machine learning:
– Keynote: Anne Martel will talk about AI for medical image analysis: Living with limited data
– Debate: The role of AI in medicine
– Panel: Commercialization of AI-enabled medical imaging technology
Throughout the symposium, there will also be talks, pitches and posters on AI, deep learning and machine learning.
We hope you can join us at ImNO 2024!
This forum focused on computational pathology for outcome prediction in cancer. Mattias Rantalainen was the first speaker talking about his research program and the work of Stratipath predictive models that are being translated into the clinic. Faisal Mahmood, the second speaker, spoke about the work in his lab that uses machine learning, data fusion and medical image analysis to develop streamlined workflows for objective diagnosis, prognosis, and biomarker discovery. The presentations were followed by questions and answers about the talks, and discussion focused on lessons learned, opportunities to collaborate, and sharing of data.
This forum focused on translating AI-based innovations in medicine into the commercial world and the clinic. The first speaker, Qian Cao, spoke about developing AI-enabled software as a medical device and the FDA Centre for Devices and Radiological Health’s perspective on AI/ML devices. Our second speaker, Eli Gibson, spoke about the complexities of translating AI/ML ideas from a manufacturer’s perspective and showcase examples. The presentations were followed by questions and answers about the talks, and discussion focused on lessons learned, opportunities to collaborate, and sharing of data.
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