Histogram-Based Unsupervised Domain Adaptation for Medical Image Classification
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Domain shift is a common problem in machine learning and medical imaging. Currently one of the most popular domain adaptation approaches is the domain-invariant mapping method using generative adversarial networks (GANs). These methods deploy some variation of a GAN to learn target domain distributions which work on pixel level. However, they often produce too complicated or unnecessary transformations. This paper is based on the hypothesis that most domain shifts in medical images are variations of global intensity changes which can be captured by transforming histograms along with individual pixel intensities. We propose a histogram-based GAN methodology for domain adaptation that outperforms standard pixel-based GAN methods in classifying chest x-rays from various heterogeneous target domains.
Original language | English |
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Title of host publication | Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings |
Editors | Linwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li |
Publisher | Springer |
Publication date | 2022 |
Pages | 755-764 |
ISBN (Print) | 9783031164484 |
DOIs | |
Publication status | Published - 2022 |
Event | 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 - Singapore, Singapore Duration: 18 Sep 2022 → 22 Sep 2022 |
Conference
Conference | 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 |
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Land | Singapore |
By | Singapore |
Periode | 18/09/2022 → 22/09/2022 |
Series | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 13437 LNCS |
ISSN | 0302-9743 |
Bibliographical note
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
- Histogram layer, Lung disease classification, Unsupervised domain adaptation
Research areas
ID: 322796553