Skip to main content


06.12.2023 | 14:00 - 15:00 (CET)

AI-Cafe presents: Generalizability, interpretability and interaction with clinicians: Some essential ingredients for developing medical imaging models

Vincent Andrearczyk, Ph.D.
(Associate researcher at the institute of Informatics, HES-SO Valais)

Medical imaging is an essential step in patient care, from diagnostic and treatment planning to follow-up, allowing doctors to assess organs, tissue and blood vessels non-invasively. AI capabilities to analyze medical images are extremely promising for assisting clinicians in their daily routines. 
This presentation introduces some of the essential ingredients for developing reliable medical imaging AI models with a focus on generalizability, interpretability and interaction with clinicians. 
Generalizability refers to the capacity of the models to adapt to new, previously unseen data, for instance, images coming from a new machine or hospital.
Interpretability refers to the translation of the working principles and outcomes of the models in human-understandable terms. Finally, the involvement of clinicians, in all phases of a model development and evaluation is crucial to ensure the utility, usability and alignment of the solutions. 
This talk will cover all these topics and their integration in various tasks to foster patient care. I will give concrete examples including brain lesion management based on MRI analysis, and head and neck tumor segmentation and outcome prediction from PET/CT images.


Vincent Andrearczyk

Vincent Andrearczyk, Ph.D., is associate researcher at the institute of Informatics, HES-SO Valais. His research explores new synergies between technology and societal needs, particularly in artificial intelligence and machine learning, applied to medical imaging. The goal of his work is to address complex medical challenges, such as detecting cancer or other diseases in medical images and assisting clinicians in treatment planning and patient follow-up. Invariance to image transformations, integration of multi-modal data, data harmonization and model interpretability are of particular interest in his research.