Abstract
Computational histopathology studies have shown that stain color variations considerably hamper the performance. Stain color variations indicate the slides exhibit greatly different color appearance due to the diversity of chemical stains, staining procedures, and slide scanners. Previous approaches tend to improve model robustness via data augmentation or stain color normalization. However, they still suffer from generalization to new domains with unseen stain colors. In this study, we address the issue of unseen color domain generalization in histopathology images by encouraging the model to adapt varied stain colors. To this end, we propose a novel data augmentation method, stain mix-up, which incorporates the stain colors of unseen domains into training data. Unlike previous mix-up methods employed in computer vision, the proposed method constructs the combination of stain colors without using any label information, hence enabling unsupervised domain generalization. Extensive experiments are conducted and demonstrate that our method is general enough to different tasks and stain methods, including H&E stains for tumor classification and hematological stains for bone marrow cell instance segmentation. The results validate that the proposed stain mix-up can significantly improves the performance on the unseen domains.
Keywords
- Domain generalization
- Mix-up
- Stain color
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Acknowledgement
We thank Wen-Chien Chou M.D.(National Taiwan University Hospital), Ta-Chuan Yu M.D.(National Taiwan University Hospital Yunlin Branch) and Poshing Lee M.D.(Department of Hematopathology, BioReference) for Hema dataset construction. This paper was supported in part by the Ministry of Science and Technology, Taiwan, under Grants MOST 110-2634-F-007-015 and MOST 109-2221-E-009-113-MY3.
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Chang, JR. et al. (2021). Stain Mix-Up: Unsupervised Domain Generalization for Histopathology Images. In: , et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12903. Springer, Cham. https://doi.org/10.1007/978-3-030-87199-4_11
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