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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
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
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
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