Patch Stitching Data Augmentation for Cancer Classification in Pathology Images
Journal:
arXiv
Published Date:
Feb 22, 2025
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.
