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Neural Stain-Style Transfer Learning using GAN for Histopathological Images

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arxiv 1710.08543 v2 pith:KEMRHRBD submitted 2017-10-23 cs.CV cs.AIcs.LG

Hyungjoo Cho , Sungbin Lim , Gunho Choi , Hyunseok Min This is my paper · ORCID
classification cs.CVcs.AIcs.LG
keywords modelhistopathologicalimagesstain-stylelossonlytransfertumor
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Performance of data-driven network for tumor classification varies with stain-style of histopathological images. This article proposes the stain-style transfer (SST) model based on conditional generative adversarial networks (GANs) which is to learn not only the certain color distribution but also the corresponding histopathological pattern. Our model considers feature-preserving loss in addition to well-known GAN loss. Consequently our model does not only transfers initial stain-styles to the desired one but also prevent the degradation of tumor classifier on transferred images. The model is examined using the CAMELYON16 dataset.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Staining normalization in histopathology: Method benchmarking using multicenter dataset

    eess.IV 2025-06 conditional novelty 7.0 of 10

    A 66-laboratory H&E staining dataset reveals broad stain variation, and benchmarking shows a simple histogram-matching method outperforms GAN-based normalization methods.

  2. Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology

    eess.IV 2024-11 conditional novelty 6.0 of 10

    Adding nuclear-segmentation-mask supervision with embedding alignment during training improves out-of-domain cancer classification in histopathology.

  3. MorphGen: Morphology-Guided Representation Learning for Robust Single-Domain Generalization in Histopathological Cancer Classification

    cs.CV 2025-08 conditional novelty 5.0 of 10

    MorphGen uses supervised contrastive learning to align histopathology images with nuclear masks and applies SWA, reporting improved out-of-domain cancer classification accuracy on CAMELYON17, BCSS, and OCELOT.

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