Patch Stitching Data Augmentation for Cancer Classification in Pathology Images

Jiamu Wang, Chang Su Kim, Jin Tae Kwak

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish
Title of host publicationMedical Imaging 2024
Subtitle of host publicationDigital and Computational Pathology
EditorsJohn E. Tomaszewski, Aaron D. Ward
ISBN (Electronic)9781510671706
Publication statusPublished - 2024
EventMedical Imaging 2024: Digital and Computational Pathology - San Diego, United States
Duration: 2024 Feb 192024 Feb 21

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2024: Digital and Computational Pathology
Country/TerritoryUnited States
CitySan Diego

Bibliographical note

Publisher Copyright:
© 2024 SPIE.


  • Cancer Classification
  • Data Augmentation
  • Data Imbalance

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging


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