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.
| Author: | Robert MendelORCiD, Luis Antonio de Souza Jr.ORCiD, David Rauber, João Paulo PapaORCiD, Christoph PalmORCiDGND |
|---|---|
| DOI: | https://doi.org/10.1007/978-3-658-33198-6_43 |
| ISBN: | 978-3-658-33197-9 |
| 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) |
| Language: | English |
| 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): |
