July 11th, 2024 (2-6 pm)
Organized by the INFORM project
“Interpretability of Deep Neural Networks for Radiomics”
Neo Christopher Chung1, Mathieu Hatt2 and Panagiotis Papadimitroulas3
1 Faculty of Mathematics, Informatics and Mechanics of the University of Warsaw, Poland
2 LaTIM, INSERM UMR 1101, Univ Brest, Brest, France
3 BIOEMTECH, Athens, Greece, https://bioemtech.com/
Registration: free
Two types of submissions (format short abstract 3000 char. max) are welcome:
- Short abstract describing a study or a project focusing on the development, the evaluation and/or the use of explainability methods or interpretability tools, in the context of medical imaging/radiotherapy, health data, clinical applications.
- A use case to present and discuss during the round table in the second part of the workshop to develop an appropriate explainability/interpretability approach dedicated to the use case. The description should cover the application, available data, objective/task, machine/deep learning algorithms used and models developed.
Submissions and/or registrations requests (name, position, affiliation) to be sent to mathieu.hatt@inserm.fr until June 17, 2024
Planning:
2:00 - 2:30 pm |
Introductory talk: “Explainability, why it matters” |
2:30 - 4:10 pm |
Talks of submitted abstracts (20’ each, 15’ for presentation, 5’ for discussion) |
4:10 - 4:30 pm |
Coffee break |
4:30 - 6:00 pm |
Round table/open discussion (submitted use cases).
Short presentation of use cases (10min), followed by discussion. |
Location:
Location of the workshop will be near the ICCR 2024 congress venue.
Bibliothèque Universitaire Lyon 1
Campus de la Doua, 20 avenue Gaston Berger, 69100 Villeurbanne, Lyon France
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