Abstract: Semi-supervised Segmentation Based on Error-correcting Supervision

  • Pixel-level classification is an essential part of computer vision. For learning from labeled data, many powerful deep learning models have been developed recently. In this work, we augment such supervised segmentation models by allowing them to learn from unlabeled data. Our semi-supervised approach, termed Error-Correcting Supervision, leverages a collaborative strategy. Apart from the supervised training on the labeled data, the segmentation network is judged by an additional network.

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Author:Robert Mendel, Luis Antonio De Souza Jr.ORCiD, David Rauber, João Paulo PapaORCiD, Christoph PalmORCiDGND
Parent Title (English):Bildverarbeitung für die Medizin 2021. Proceedings, German Workshop on Medical Image Computing, Regensburg, March 7-9, 2021
Publisher:Springer Vieweg
Place of publication:Wiesbaden
Document Type:conference proceeding (presentation, abstract)
Year of first Publication:2021
Release Date:2021/03/10
GND Keyword:Deep Learning
First Page:178
Institutes:Fakultät Informatik und Mathematik
Regensburg Center of Health Sciences and Technology - RCHST
Fakultät Informatik und Mathematik / Regensburg Medical Image Computing (ReMIC)
research focus:Lebenswissenschaften und Ethik
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG