Few-Shot Microscopy Image Cell Segmentation
Dawoud Y, Hornauer J, Carneiro G, Belagiannis V (2021)
Publication Type: Conference contribution
Publication year: 2021
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 12461 LNAI
Pages Range: 139-154
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Virtual, Online
ISBN: 9783030676698
DOI: 10.1007/978-3-030-67670-4_9
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.
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Germany (DE)
Australia (AU)How to cite
APA:
Dawoud, Y., Hornauer, J., Carneiro, G., & Belagiannis, V. (2021). Few-Shot Microscopy Image Cell Segmentation. In Yuxiao Dong, Dunja Mladenic, Craig Saunders (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 139-154). Virtual, Online: Springer Science and Business Media Deutschland GmbH.
MLA:
Dawoud, Youssef, et al. "Few-Shot Microscopy Image Cell Segmentation." Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, Virtual, Online Ed. Yuxiao Dong, Dunja Mladenic, Craig Saunders, Springer Science and Business Media Deutschland GmbH, 2021. 139-154.
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