Skip to Main content Skip to Navigation
New interface
Conference papers

POPCORN: Progressive Pseudo-labeling with Consistency Regularization and Neighboring

Abstract : 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.
Document type :
Conference papers
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-03365185
Contributor : Nicolas Papadakis Connect in order to contact the contributor
Submitted on : Thursday, October 14, 2021 - 2:52:11 PM
Last modification on : Saturday, June 25, 2022 - 10:44:20 AM
Long-term archiving on: : Saturday, January 15, 2022 - 6:36:25 PM

File

POPCORN_Arxiv_version.pdf
Files produced by the author(s)

Identifiers

Collections

CNRS | ANR

Citation

Reda Abdellah Kamraoui, Vinh-Thong Ta, Nicolas Papadakis, Fanny Compaire, José V Manjon, et al.. POPCORN: Progressive Pseudo-labeling with Consistency Regularization and Neighboring. International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'21), Sep 2021, Strasbourg (virtual), France. pp.373-382, ⟨10.1007/978-3-030-87196-3_35⟩. ⟨hal-03365185⟩

Share

Metrics

Record views

96

Files downloads

66