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https://hdl.handle.net/2440/136832
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Type: | Conference paper |
Title: | Edge-Based Self-supervision for Semi-supervised Few-Shot Microscopy Image Cell Segmentation |
Author: | Dawoud, Y. Ernst, K. Carneiro, G. Belagiannis, V. |
Citation: | Lecture Notes in Artificial Intelligence, 2022 / Huo, Y., Millis, B.A., Zhou, Y., Wang, X., Harrison, A.P., Xu, Z. (ed./s), vol.13578, pp.22-31 |
Publisher: | Springer |
Issue Date: | 2022 |
Series/Report no.: | Lecture Notes in Computer Science; 13578 |
ISBN: | 9783031169601 |
ISSN: | 0302-9743 1611-3349 |
Conference Name: | International Workshop on Medical Optical Imaging and Virtual Microscopy Image Analysis (MOVI) (18 Sep 2022 - 18 Sep 2022 : Singapore) |
Editor: | Huo, Y. Millis, B.A. Zhou, Y. Wang, X. Harrison, A.P. Xu, Z. |
Statement of Responsibility: | Youssef Dawoud, Katharina Ernst, Gustavo Carneiro, Vasileios Belagiannis |
Abstract: | Deep neural networks currently deliver promising results for microscopy image cell segmentation, but they require large-scale labelled databases, which is a costly and time-consuming process. In this work, we relax the labelling requirement by combining self-supervised with semi-supervised learning. We propose the prediction of edge-based maps for self-supervising the training of the unlabelled images, which is combined with the supervised training of a small number of labelled images for learning the segmentation task. In our experiments, we evaluate on a few-shot microscopy image cell segmentation benchmark and show that only a small number of annotated images, e.g. 10% of the original training set, is enough for our approach to reach similar performance as with the fully annotated databases on 1- to 10-shots. Our code and trained models is made publicly available https://github.com/Yussef93/EdgeSSFewShotMicroscopy. |
Keywords: | Cell segmentation; Few-shot microscopy; Semi-supervised learning |
Rights: | © 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG |
DOI: | 10.1007/978-3-031-16961-8_3 |
Grant ID: | http://purl.org/au-research/grants/arc/FT190100525 |
Published version: | https://link.springer.com/book/10.1007/978-3-031-16961-8 |
Appears in Collections: | Computer Science publications |
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