Abstract
Histopathological analysis is the present gold standard for cancer diagnosis. Accurate classification of histopathology images has great clinical significance and application value for assisting pathologists in diagnosis. However, the performance of histopathology image classification is greatly affected by data imbalance. To address this problem, we propose a novel data augmentation framework based on the diffusion model, DiscrimDiff, which expands the dataset by synthesizing images of rare classes. To compensate for the lack of discrimination ability of the diffusion model for synthesized images, we design a post-discrimination mechanism to provide image quality assurance for data augmentation. Our method significantly improves classification performance on multiple datasets. Furthermore, histomorphological features of different classes concerned by the diffusion model may provide guiding significance for pathologists in clinical diagnosis. Therefore, we visualize histomorphological features related to classification, which can be used to assist pathologist-in-training education and improve the understanding of histomorphology.
X. Guan, Y. Wang and Y. Lin—Co-first authors.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (62031023), in part by the Shenzhen Science and Technology Project (JCYJ20200109142808034 &GXWD20220818170353009), and in part by Guangdong Special Support (2019TX05X187).
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Guan, X., Wang, Y., Lin, Y., Zhang, Y. (2024). Data Augmentation Based on DiscrimDiff for Histopathology Image Classification. In: Xue, Y., Chen, C., Chen, C., Zuo, L., Liu, Y. (eds) Data Augmentation, Labelling, and Imperfections. MICCAI 2023. Lecture Notes in Computer Science, vol 14379. Springer, Cham. https://doi.org/10.1007/978-3-031-58171-7_6
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