AI for Multi-center Data #
Wilson Silva, Valentina Corbetta, Daan Boeke
Background #
It is extremely common that a deep learning algorithm performs well in one medical dataset but is unable to generalize to a different dataset. This is mainly due to different medical datasets being characterised by different sources (e.g., scanners) and different populations. AI for multi-center data focuses on the development of robust algorithms, techniques to share data, and understanding of AI decisions.
Objectives #
Generalizability #
Make AI-models work across different centers. Development of robust models:
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Through disentanglement representation learning;
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Inductive bias (embedding expert knowledge into the models);
Privacy #
Protect patient privacy:
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Develop visual anonymization models to share realistic synthetic data;
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Propose federated learning algorithms to work with multi-center data without data sharing;
Explainable AI #
Make it easier to understand and trust AI decisions:
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Case-based Interpretability (factual and counterfactual explanations);
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Semantic concept discovery;
People #
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Wilson Silva, PhD. Wilson is the PI behind the AI for multi-center data research line.
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Valentina Corbetta (PhD student, University of Maastricht) [Dec 2022 - ongoing]. Valentina is exploring disentanglement learning techniques for segmentation and classification algorithms in the context of medical multi-center data.
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Daan Boeke (PhD student, University of Maastricht) [March 2023 - ongoing]. Daan is exploring the potential of AI for immune micro-environment characterization and prediction of treatment response.
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Melanie Groeneveld (MSc student, VU Amsterdam) [Jan 2023 - ongoing]. Melanie’s research focuses on embedding expert knowledge into AI models for increased robustness and interpretability.
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Filipe Campos (MSc student, University of Porto)
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Rita Mendes (MSc student, University of Porto)
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Elena Mordmillovich (BSc student) [Sep 2023 - ongoing]. Elena is developing language models to analyse radiology reports.
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Daniel Silva (MSc student, University of Porto) [Feb 2023 - Sep 2023]. Daniel worked on disentangling scanner and disease features to come up with robust models for disease diagnosis.
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Isabela Miranda (MSc student, University of Porto) [Feb 2023 - Sep 2023]. Isabela worked on self-supervised techniques to improve model performance and robustness in breast multi-center data.
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Laura Latorre (MSc student, VU Amsterdam) [Feb 2023 - Aug 2023]. Laura’s research focused on federated learning AI models and how we move towards federated explainability.
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Lena Stelter (Intern) [Feb 2023 - July 2023]. Lena developed federated polyp segmentation models for colonoscopy multi-center data.