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https://hdl.handle.net/2440/131365
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Type: | Conference paper |
Title: | Few-shot microscopy image cell segmentation |
Author: | Dawoud, Y. Hornauer, J. Carneiro, G. Belagiannis, V. |
Citation: | Lecture Notes in Artificial Intelligence, 2021 / Dong, Y., Ifrim, G., Mladenic, D., Saunders, C., VanHoecke, S. (ed./s), vol.12461, pp.139-154 |
Publisher: | Springer |
Publisher Place: | Cham, Switzerland |
Issue Date: | 2021 |
Series/Report no.: | Lecture Notes in Computer Science; 12461 |
ISBN: | 9783030676698 |
ISSN: | 0302-9743 1611-3349 |
Conference Name: | Joint European Conference on Machine Learning and Knowledge Discover in Databases (ECML PKDD) (14 Sep 2020 - 18 Sep 2020 : virtual online) |
Editor: | Dong, Y. Ifrim, G. Mladenic, D. Saunders, C. VanHoecke, S. |
Statement of Responsibility: | Youssef Dawoud, Julia Hornauer, Gustavo Carneiro, Vasileios Belagiannis |
Abstract: | Automatic cell segmentation in microscopy images works well with the support of deep neural networks trained with full supervision. Collecting and annotating images, though, is not a sustainable solution for every new microscopy database and cell type. Instead, we assume that we can access a plethora of annotated image data sets from different domains (sources) and a limited number of annotated image data sets from the domain of interest (target), where each domain denotes not only different image appearance but also a different type of cell segmentation problem. We pose this problem as meta-learning where the goal is to learn a generic and adaptable few-shot learning model from the available source domain data sets and cell segmentation tasks. The model can be afterwards fine-tuned on the few annotated images of the target domain that contains different image appearance and different cell type. In our meta-learning training, we propose the combination of three objective functions to segment the cells, move the segmentation results away from the classification boundary using cross-domain tasks, and learn an invariant representation between tasks of the source domains. Our experiments on five public databases show promising results from 1- to 10-shot meta-learning using standard segmentation neural network architectures. |
Keywords: | Cell segmentation; Microscopy image; Few-shot learning |
Rights: | © Springer Nature Switzerland AG 2021 |
DOI: | 10.1007/978-3-030-67670-4_9 |
Grant ID: | http://purl.org/au-research/grants/arc/FT190100525 |
Published version: | https://link.springer.com/book/10.1007/978-3-030-67670-4 |
Appears in Collections: | Aurora harvest 4 Computer Science publications |
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