Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/131365
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dc.contributor.author | Dawoud, Y. | - |
dc.contributor.author | Hornauer, J. | - |
dc.contributor.author | Carneiro, G. | - |
dc.contributor.author | Belagiannis, V. | - |
dc.contributor.editor | Dong, Y. | - |
dc.contributor.editor | Ifrim, G. | - |
dc.contributor.editor | Mladenic, D. | - |
dc.contributor.editor | Saunders, C. | - |
dc.contributor.editor | VanHoecke, S. | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Lecture Notes in Artificial Intelligence, 2021 / Dong, Y., Ifrim, G., Mladenic, D., Saunders, C., VanHoecke, S. (ed./s), vol.12461, pp.139-154 | - |
dc.identifier.isbn | 9783030676698 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.issn | 1611-3349 | - |
dc.identifier.uri | http://hdl.handle.net/2440/131365 | - |
dc.description.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. | - |
dc.description.statementofresponsibility | Youssef Dawoud, Julia Hornauer, Gustavo Carneiro, Vasileios Belagiannis | - |
dc.language.iso | en | - |
dc.publisher | Springer | - |
dc.relation.ispartofseries | Lecture Notes in Computer Science; 12461 | - |
dc.rights | © Springer Nature Switzerland AG 2021 | - |
dc.source.uri | https://link.springer.com/book/10.1007/978-3-030-67670-4 | - |
dc.subject | Cell segmentation; Microscopy image; Few-shot learning | - |
dc.title | Few-shot microscopy image cell segmentation | - |
dc.type | Conference paper | - |
dc.contributor.conference | Joint European Conference on Machine Learning and Knowledge Discover in Databases (ECML PKDD) (14 Sep 2020 - 18 Sep 2020 : virtual online) | - |
dc.identifier.doi | 10.1007/978-3-030-67670-4_9 | - |
dc.publisher.place | Cham, Switzerland | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/FT190100525 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Carneiro, G. [0000-0002-5571-6220] | - |
Appears in Collections: | Aurora harvest 4 Computer Science publications |
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