Workshop on explainability of machine/deep learning models for medical applications

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,

Registration: free

Two types of submissions (format short abstract 3000 char. max) are welcome:

  1. 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.
  2. 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 until June 17, 2024


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 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

Because of construction work, there will be no trams serving the workshop place. You will have to use the buses.