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Histogram-Based Unsupervised Domain Adaptation for Medical Image Classification. / Diao, Pengfei; Pai, Akshay; Igel, Christian; Krag, Christian Hedeager.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings. red. / Linwei Wang; Qi Dou; P. Thomas Fletcher; Stefanie Speidel; Shuo Li. Springer, 2022. s. 755-764 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 13437 LNCS).
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
Diao, P, Pai, A
, Igel, C & Krag, CH 2022,
Histogram-Based Unsupervised Domain Adaptation for Medical Image Classification. i L Wang, Q Dou, PT Fletcher, S Speidel & S Li (red),
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings. Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), bind 13437 LNCS, s. 755-764, 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, Singapore, Singapore,
18/09/2022.
https://doi.org/10.1007/978-3-031-16449-1_72APA
Diao, P., Pai, A.
, Igel, C., & Krag, C. H. (2022).
Histogram-Based Unsupervised Domain Adaptation for Medical Image Classification. I L. Wang, Q. Dou, P. T. Fletcher, S. Speidel, & S. Li (red.),
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings (s. 755-764). Springer. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Bind 13437 LNCS
https://doi.org/10.1007/978-3-031-16449-1_72Vancouver
Diao P, Pai A
, Igel C, Krag CH.
Histogram-Based Unsupervised Domain Adaptation for Medical Image Classification. I Wang L, Dou Q, Fletcher PT, Speidel S, Li S, red., Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings. Springer. 2022. s. 755-764. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 13437 LNCS).
https://doi.org/10.1007/978-3-031-16449-1_72Author
Diao, Pengfei ; Pai, Akshay ; Igel, Christian ; Krag, Christian Hedeager. / Histogram-Based Unsupervised Domain Adaptation for Medical Image Classification. Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings. red. / Linwei Wang ; Qi Dou ; P. Thomas Fletcher ; Stefanie Speidel ; Shuo Li. Springer, 2022. s. 755-764 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 13437 LNCS).
Bibtex
@inproceedings{4f48064afb5542479c3739de447ce923,
title = "Histogram-Based Unsupervised Domain Adaptation for Medical Image Classification",
abstract = "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.",
keywords = "Histogram layer, Lung disease classification, Unsupervised domain adaptation",
author = "Pengfei Diao and Akshay Pai and Christian Igel and Krag, {Christian Hedeager}",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 ; Conference date: 18-09-2022 Through 22-09-2022",
year = "2022",
doi = "10.1007/978-3-031-16449-1_72",
language = "English",
isbn = "9783031164484",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "755--764",
editor = "Linwei Wang and Qi Dou and Fletcher, {P. Thomas} and Stefanie Speidel and Shuo Li",
booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings",
address = "Switzerland",
}
RIS
TY - GEN
T1 - Histogram-Based Unsupervised Domain Adaptation for Medical Image Classification
AU - Diao, Pengfei
AU - Pai, Akshay
AU - Igel, Christian
AU - Krag, Christian Hedeager
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Histogram layer
KW - Lung disease classification
KW - Unsupervised domain adaptation
U2 - 10.1007/978-3-031-16449-1_72
DO - 10.1007/978-3-031-16449-1_72
M3 - Article in proceedings
AN - SCOPUS:85139020088
SN - 9783031164484
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 755
EP - 764
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings
A2 - Wang, Linwei
A2 - Dou, Qi
A2 - Fletcher, P. Thomas
A2 - Speidel, Stefanie
A2 - Li, Shuo
PB - Springer
T2 - 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
Y2 - 18 September 2022 through 22 September 2022
ER -
