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Recent advances in machine learning for computer aided diagnosis and prognosis based on medical imaging

Nous vous rappelons que, afin de garantir l'accès de tous les inscrits aux salles de réunion, l'inscription aux réunions est gratuite mais obligatoire.

Inscriptions closes à cette réunion.


26 personnes membres du GdR ISIS, et 22 personnes non membres du GdR, sont inscrits à cette réunion.
Capacité de la salle : 200 personnes.

Réunion d'animation en visio-conférence

La réunion aura lieu en visioconférence. Cependant pour des raisons techniques liées au nombre de connexions simultanées, l'inscription aux réunions est gratuite, mais obligatoire.

Les identifiants de connexion sont communiquées par mail aux inscrits la veille ou le matin de la réunion.


Réunion reportée en octobre 2020 (date à préciser).

In recent years, artificial intelligence, especially machine learning has received a lot of attention to explore and structure multidimensional and multimodality medical imaging data, especially for the design of diagnosis models, aiming at detecting, localizing and characterizing pathological patterns in the data. Some academic works also recently explored the potential of artificial intelligence for predicting the course and outcome of diseases. This one-day workshop intends to gather researchers in deep machine learning, computer vision and/or medical image analysis as well companies and AI-based startups in the medical image field. We will start by reviewing state-of-the art achievements in the domain of computer aided diagnosis (including patient screening, detection, segmentation..) and prognosis models based on medical imaging for different clinical applications. Then, we will cover some challenges that need to be addressed to foster the development of these diagnosis and prognosis models. This includes methodological questions such as uncertainty and interpretability of the deep learning based models, as well as strategies regarding the evaluation framework of models performance (challenges, standardisation of the performance metrics..) and the access to structured medical image database.

This one-day workshop is organized jointly by the action "Analysis, processing and decision for massive and multimodal data in life sciences" of theme B Image and Vision, and the transversal theme T Machine learning for signal and image analysis.

We will have three keynote presentations by:

Mathieu De Craene, Research Scientist at Philips, Paris

Pierrick Coupé, DR CRNS au LabRI, Bordeaux

Ivana Isgum, University Professor AI and Medical Imaging at Amsterdam University Medical Center

This one-day workshop also includes communications for which we are launching a call for contributions. If you wish to present your work, please send your proposal by May 11, 2020 (title, authors, affiliation, 15-line summary) to the organizers:


Résumés des contributions

Date : 2020-10-03

Lieu : Amphi Abbé Grégoire, CNAM, 292 rue St Martin, 75 003 Paris

Thèmes scientifiques :
B - Image et Vision
T - Apprentissage pour l'analyse du signal et des images

Inscriptions closes à cette réunion.

(c) GdR 720 ISIS - CNRS - 2011-2020.