Entrepôts, Représentation et Ingénierie des Connaissances
Séminaires d'ERIC
Pour toute question concernant les séminaires, vous pouvez contacter Nadia Kabachi (Nadia.Kabachi@univ-lyon1.fr) et Jairo Cugliari (Jairo.Cugliari@univ-lyon2.fr)

Recherche approfondie

par Année
par Type
par Thème
feed

(1) Presentation(s)

-

Lun. 14/01/2019 11:00 K71, Bâtiment K, RdC

Séminaire
GANAYE Pierre-antoine (Creatis - INSA Lyon)
Semi-supervised learning for segmentation under semantic constraint

Sommaire:

Image segmentation based on convolutional neural networks
is proving to be a powerful and efficient solution for medical applications.
However, the lack of annotated data, presence of artifacts and variability
in appearance can still result in inconsistencies during the inference. We
choose to take advantage of the invariant nature of anatomical struc-
tures, by enforcing a semantic constraint to improve the robustness of
the segmentation. The proposed solution is applied on a brain struc-
tures segmentation task, where the output of the network is constrained
to satisfy a known adjacency graph of the brain regions. This criteria
is introduced during the training through an original penalization loss
named NonAdjLoss. With the help of a new metric, we show that the
proposed approach significantly reduces abnormalities produced during
the segmentation. Additionally, we demonstrate that our framework can
be used in a semi-supervised way, opening a path to better generalization
to unseen data.


Pour plus d'informations, merci de contacter Cugliari J.