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Volume 25
Issue 9
10.3390/s25092856
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Bridging Domain Gaps in Computational Pathology: A Comparative Study of Adaptation Strategies

by
João D. Nunes
1,2,*,
Diana Montezuma
3,4,
Domingos Oliveira
3,
Tania Pereira
1,5,
Inti Zlobec
6,
Isabel Macedo Pinto
3 and
Jaime S. Cardoso
1,2
1
Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal
2
Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
3
IMP Diagnostics, 4150-146 Porto, Portugal
4
Cancer Biology and Epigenetics Group, Research Center of Portuguese Oncology Institute of Porto/RISE@Research Center of Portuguese Oncology Institute of Porto (Health Research Network), Portuguese Oncology Institute of Porto/Porto Comprehensive Cancer Centre Raquel Seruca, R. Dr. António Bernardino de Almeida, 4200-072 Porto, Portugal
5
FCTUC—Faculty of Sciences and Technology, University of Coimbra, 3004-516 Coimbra, Portugal
6
Institute of Tissue Medicine and Pathology, University of Bern, 3008 Bern, Switzerland
*
Author to whom correspondence should be addressed.
Sensors 2025, 25(9), 2856; https://doi.org/10.3390/s25092856
Submission received: 25 February 2025 / Revised: 19 April 2025 / Accepted: 29 April 2025 / Published: 30 April 2025
(This article belongs to the Section Sensing and Imaging)
Review Reports Versions Notes

Abstract

Due to the high variability in Hematoxylin and Eosin (H&E)-stained Whole Slide Images (WSIs), hidden stratification, and batch effects, generalizing beyond the training distribution is one of the main challenges in Deep Learning (DL) for Computational Pathology (CPath). But although DL depends on large volumes of diverse and annotated data, it is common to have a significant number of annotated samples from one or multiple source distributions, and another partially annotated or unlabeled dataset representing a target distribution for which we want to generalize, the so-called Domain Adaptation (DA). In this work, we focus on the task of generalizing from a single source distribution to a target domain. As it is still not clear which domain adaptation strategy is best suited for CPath, we evaluate three different DA strategies, namely FixMatch, CycleGAN, and a self-supervised feature extractor, and show that DA is still a challenge in CPath.
Keywords: domain adaptation; weakly supervised learning; consistency regularization; multiple instance learning; computational pathology domain adaptation; weakly supervised learning; consistency regularization; multiple instance learning; computational pathology

Share and Cite

MDPI and ACS Style

Nunes, J.D.; Montezuma, D.; Oliveira, D.; Pereira, T.; Zlobec, I.; Pinto, I.M.; Cardoso, J.S. Bridging Domain Gaps in Computational Pathology: A Comparative Study of Adaptation Strategies. Sensors 2025, 25, 2856. https://doi.org/10.3390/s25092856

AMA Style

Nunes JD, Montezuma D, Oliveira D, Pereira T, Zlobec I, Pinto IM, Cardoso JS. Bridging Domain Gaps in Computational Pathology: A Comparative Study of Adaptation Strategies. Sensors. 2025; 25(9):2856. https://doi.org/10.3390/s25092856

Chicago/Turabian Style

Nunes, João D., Diana Montezuma, Domingos Oliveira, Tania Pereira, Inti Zlobec, Isabel Macedo Pinto, and Jaime S. Cardoso. 2025. "Bridging Domain Gaps in Computational Pathology: A Comparative Study of Adaptation Strategies" Sensors 25, no. 9: 2856. https://doi.org/10.3390/s25092856

APA Style

Nunes, J. D., Montezuma, D., Oliveira, D., Pereira, T., Zlobec, I., Pinto, I. M., & Cardoso, J. S. (2025). Bridging Domain Gaps in Computational Pathology: A Comparative Study of Adaptation Strategies. Sensors, 25(9), 2856. https://doi.org/10.3390/s25092856

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