Patch stitching data augmentation for cancer classification in pathology images

Jiamu Wang; Chang-Su Kim; Jin Tae Kwak

doi:10.1117/12.3006330

3 April 2024 Patch stitching data augmentation for cancer classification in pathology images

Jiamu Wang, Chang-Su Kim, Jin Tae Kwak

Proceedings Volume 12933, Medical Imaging 2024: Digital and Computational Pathology; 129331B (2024) https://doi.org/10.1117/12.3006330
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Computational pathology, integrating computational methods and digital imaging, has shown to be effective in advancing disease diagnosis and prognosis. In recent years, the development of machine learning and deep learning has greatly bolstered the power of computational pathology. However, there still remains the issue of data scarcity and data imbalance, which can have an adversarial effect on any computational method. In this paper, we introduce an efficient and effective data augmentation strategy to generate new pathology images from the existing pathology images and thus enrich datasets without additional data collection or annotation costs. To evaluate the proposed method, we employed two sets of colorectal cancer datasets and obtained improved classification results, suggesting that the proposed simple approach holds the potential for alleviating the data scarcity and imbalance in computational pathology.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jiamu Wang, Chang-Su Kim, and Jin Tae Kwak "Patch stitching data augmentation for cancer classification in pathology images", Proc. SPIE 12933, Medical Imaging 2024: Digital and Computational Pathology, 129331B (3 April 2024); https://doi.org/10.1117/12.3006330

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