Weakly supervised medical image segmentation through dense combinations of dense pseudo-l-abels

Conference item

Annotating a large amount of medical imaging data thoroughly for training purposes can be expensive, particularly for medical image segmentation tasks; whereas obtaining scribbles, a less precise form of annotation, is more feasible for clinicians. Nevertheless, training semantic segmentation networks with limited-signal supervision remains a technical challenge. In this paper, we present an innovative scribble-supervised image segmentation via densely ensembling dense pseudos called Collaborative Hybrid Networks(CHNets), which consists of groups of CNN- and ViT-based segmentation networks. A simple yet efficient densely collaboration scheme is introduced to ensemble dense pseudo label to expand dataset allowing full-signal supervision. Additionally, internal consistency and external consistency training among networks are proposed to ensure that each network is beneficial to the other, resulting in a significant improvement. Our experiments on a public MRI benchmark dataset demonstrate that our proposed approach outperforms other weakly-supervised methods on various metrics.

Authors: Wang, Z; Voiculescu, I

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer
• Volume: 14314
• Pages: 1-10
• Chapter number: Proceedings of the 1st Workshop in Data Engineering in Medical Imaging (DEMI 2023) at the 26th Medical Image Computing and Computer Assisted Intervention (MICCAI 2023)
• Series: Lecture Notes in Computer Science
• Publication date: 2023-10-01
• Acceptance date: 2023-07-27
• Event title: 1st Workshop in Data Engineering in Medical Imaging (DEMI 2023) at the 26th Medical Image Computing and Computer Assisted Intervention (MICCAI 2023)
• Event location: Vancouver, BC, Canada
• Event website: https://demi-workshop.github.io/
• Event start date: 2023-10-08
• Event end date: 2023-10-12
• DOI: 10.1007/978-3-031-44992-5_1
• EISSN: 1611-3349
• ISSN: 0302-9743
• EISBN: 978-3-031-44992-5
• ISBN: 978-3-031-44991-8
• Language: English
• Keywords: FFR