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

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.