The event will take place on Thursday, September 22nd 2022 at 1:30 pm, room A301, Illkirch Campus.
Abstract
Current state-of-the-art deep learning methods are data-hungry approaches which require huge annotated data collections to perform well. Nevertheless, digital histopathology, like the other fields of the medical domain, is known for its scarcity of data. Moreover, considering the variations that can occur due to the staining process and staining protocols, already collected and annotated datasets can only be reused with limited success. Such stain variation represents a source of domain shift and significantly affects deep learning-based solutions in practice. This thesis investigates the potential of Generative Adversarial Networks (GANs) in two directions for addressing these problems --- stain transfer to enable reusing already available data collections; and developing stain invariant solutions which would alleviate the need for additional data acquisition or annotations.
Jury members
- Prof. Cédric WEMMERT, Université de Strasbourg - PhD supervisor
- Prof. Srdjan STANKOVIĆ, University of Belgrade, Serbia - PhD co-supervisor
- Dr Xavier DESCOMBES, Research Director, INRIA Sophia Antipolis - Reviewer
- Prof. Maja TEMERINAC OTT, Hochschule Furtwangen, Germany - Reviewer
- Dr Odyssée MERVEILLE, INSA Lyon - Examiner
- Dr Sarah LECLERC, Université de Bourgogne - Examiner
- Dr Thomas LAMPERT, Université de Strasbourg - Invited member
Join through videoconference
- Meeting ID: 942 0693 2195
- Password: xkn11A