Combined Learning for Similar Tasks with Domain-Switching Networks
Bug D, Eschweiler D, Liu Q, Schock J, Weninger L, Feuerhake F, Schueler J, Stegmaier J, Merhof D (2019)
Publication Type: Conference contribution
Publication year: 2019
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 11768 LNCS
Pages Range: 565-572
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Shenzhen, CHN
ISBN: 9783030322533
DOI: 10.1007/978-3-030-32254-0_63
Abstract
We introduce a domain switch for deep neural networks that enables to re-weight convolutional kernels for an input of a known domain. This technique is designed to address re-occurring tasks across multiple domains that are known at runtime and to incorporate them into a single, domain-spanning network. We evaluate this approach in three distinct tasks, namely combined cell nuclei analysis across different stains and fluorescence images, facial landmark detection in grayscale and thermal infrared images, and the BraTS challenge where we treat different recording institutions as domains. We found that conventional U-nets trained on multiple domains perform similar to domain-specific U-nets. Our method improves the results in facial landmark detection significantly, but no change is measured in the other two experiments compared to multi-domain U-nets.
Involved external institutions
Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen
Germany (DE)
Medizinische Hochschule Hannover (MHH) / Hannover Medical School
Germany (DE)
Charles River Laboratories (CRL) (Germany)
Germany (DE)
Germany (DE)
Medizinische Hochschule Hannover (MHH) / Hannover Medical School
Germany (DE)
Charles River Laboratories (CRL) (Germany)
Germany (DE)
How to cite
APA:
Bug, D., Eschweiler, D., Liu, Q., Schock, J., Weninger, L., Feuerhake, F.,... Merhof, D. (2019). Combined Learning for Similar Tasks with Domain-Switching Networks. In Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 565-572). Shenzhen, CHN: Springer Science and Business Media Deutschland GmbH.
MLA:
Bug, Daniel, et al. "Combined Learning for Similar Tasks with Domain-Switching Networks." Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, CHN Ed. Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou, Springer Science and Business Media Deutschland GmbH, 2019. 565-572.
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