POPCORN: Progressive Pseudo-labeling with Consistency Regularization and Neighboring
hal.structure.identifier | Laboratoire Bordelais de Recherche en Informatique [LaBRI] | |
dc.contributor.author | KAMRAOUI, Reda Abdellah | |
hal.structure.identifier | Laboratoire Bordelais de Recherche en Informatique [LaBRI] | |
dc.contributor.author | TA, Vinh-Thong | |
hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
dc.contributor.author | PAPADAKIS, Nicolas | |
dc.contributor.author | COMPAIRE, Fanny | |
hal.structure.identifier | ITACA | |
dc.contributor.author | MANJON, José V | |
hal.structure.identifier | Laboratoire Bordelais de Recherche en Informatique [LaBRI] | |
dc.contributor.author | COUPÉ, Pierrick | |
dc.date | 2021 | |
dc.date.accessioned | 2024-04-04T02:45:07Z | |
dc.date.available | 2024-04-04T02:45:07Z | |
dc.date.issued | 2021 | |
dc.date.conference | 2021-09-27 | |
dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/191439 | |
dc.description.abstractEn | Semi-supervised learning (SSL) uses unlabeled data to compensate for the scarcity of annotated images and the lack of method generalization to unseen domains, two usual problems in medical segmentation tasks. In this work, we propose POPCORN, a novel method combining consistency regularization and pseudo-labeling designed for image segmentation. The proposed framework uses high-level regularization to constrain our segmentation model to use similar latent features for images with similar segmentations. POPCORN estimates a proximity graph to select data from easiest ones to more difficult ones, in order to ensure accurate pseudo-labeling and to limit confirmation bias. Applied to multiple sclerosis lesion segmentation, our method demonstrates competitive results compared to other state-of-the-art SSL strategies. | |
dc.description.sponsorship | Apprentissage profond pour la volumétrie cérébrale : vers le BigData en neuroscience - ANR-18-CE45-0013 | |
dc.language.iso | en | |
dc.subject.en | MS lesion segmentation | |
dc.subject.en | Consistency regularization | |
dc.subject.en | Pseudo-labeling | |
dc.subject.en | Semi-supervised Learning | |
dc.title.en | POPCORN: Progressive Pseudo-labeling with Consistency Regularization and Neighboring | |
dc.type | Communication dans un congrès | |
dc.identifier.doi | 10.1007/978-3-030-87196-3_35 | |
dc.subject.hal | Informatique [cs]/Traitement du signal et de l'image | |
dc.identifier.arxiv | 2109.06361 | |
bordeaux.page | 373-382 | |
bordeaux.volume | 12902 | |
bordeaux.hal.laboratories | Institut de Mathématiques de Bordeaux (IMB) - UMR 5251 | * |
bordeaux.institution | Université de Bordeaux | |
bordeaux.institution | Bordeaux INP | |
bordeaux.institution | CNRS | |
bordeaux.conference.title | International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'21) | |
bordeaux.country | FR | |
bordeaux.conference.city | Strasbourg (virtual) | |
bordeaux.peerReviewed | oui | |
hal.identifier | hal-03365185 | |
hal.version | 1 | |
hal.invited | non | |
hal.proceedings | oui | |
hal.popular | non | |
hal.audience | Internationale | |
hal.origin.link | https://hal.archives-ouvertes.fr//hal-03365185v1 | |
bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2021&rft.volume=12902&rft.spage=373-382&rft.epage=373-382&rft.au=KAMRAOUI,%20Reda%20Abdellah&TA,%20Vinh-Thong&PAPADAKIS,%20Nicolas&COMPAIRE,%20Fanny&MANJON,%20Jos%C3%A9%20V&rft.genre=unknown |
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