Deep-Supervised Adversarial Learning-based Classification For Digital Histologic Images

Zhimin Wang, Zong Fan, Lulu Sun, Yao Hao, Hiram A. Gay, Wade L. Thorstad, Xiaowei Wang, Hua Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


High-resolution histopathological images have rich characteristics of cancer tissues and cells. Recent studies have shown that digital pathology analysis can aid clinical decision-making by identifying metastases, subtyping and grading tumors, and predicting clinical outcomes. Still, the analysis of digital histologic images remains challenging due to the imbalance of the training data, the intrinsic complexity of histology characteristics of tumor tissue, and the extremely heavy computation burden for processing extremely high-resolution whole slide imaging (WSI) images. In this study, we developed a new deep learning-based classification framework that addresses these unique challenges to support clinical decision-making. The proposed method is motivated by our recently developed adversarial learning strategy with two major innovations. First, an image pre-processing module was designed to process the high-resolution histology images to reduce computational burden and keep informative features, alleviating the risk of overfitting issues when training the network. Second, recently developed StyleGAN2 with powerful generative capability was employed to recognize complex texture patterns and stain information in histology images and learn deep classification-relevant information, further improving the classification and reconstruction performance of our method. The experimental results on three different histology image datasets for different classification tasks demonstrated superior classification performance compared to traditional deep learning-based methods, and the generality of the proposed method to be applied to various applications.

Original languageEnglish
Title of host publicationMedical Imaging 2023
Subtitle of host publicationDigital and Computational Pathology
EditorsJohn E. Tomaszewski, Aaron D. Ward
ISBN (Electronic)9781510660472
StatePublished - 2023
EventMedical Imaging 2023: Digital and Computational Pathology - San Diego, United States
Duration: Feb 19 2023Feb 23 2023

Publication series

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


ConferenceMedical Imaging 2023: Digital and Computational Pathology
Country/TerritoryUnited States
CitySan Diego


  • Deep learning-based classification
  • Digital Pathology
  • Generative Adversarial Network
  • Whole Slide Images


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