mlsanityData to 7 Oct 2026

Domain-adversarial neural networks to address the appearance variability of histopathology images

arXiv 1707.06183 ↗First posted 19 Jul 2017cs.CVcited by 41

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Abstract (arXiv)

Preparing and scanning histopathology slides consists of several steps, each with a multitude of parameters. The parameters can vary between pathology labs and within the same lab over time, resulting in significant variability of the tissue appearance that hampers the generalization of automatic image analysis methods. Typically, this is addressed with ad-hoc approaches such as staining normalization that aim to reduce the appearance variability. In this paper, we propose a systematic solution based on domain-adversarial neural networks. We hypothesize that removing the domain information from the model representation leads to better generalization. We tested our hypothesis for the problem of mitosis detection in breast cancer histopathology images and made a comparative analysis with two other approaches. We show that combining color augmentation with domain-adversarial training is a better alternative than standard approaches to improve the generalization of deep learning methods.

Key facts. Cited by 41 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Jul 2017.

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Citing papers per year: 2021: 19; 2022: 10; 2023: 6; 2024: 5; 2025: 0; 2026: 1.

41 papers.

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