An interpretable deep learning model for identifying the morphological characteristics of dMMR/MSI-H gastric cancer

Accurate tumor diagnosis by pathologists relies on identifying specific morphological characteristics. However, summarizing these unique morphological features in tumor classifications can be challenging. Although deep learning models have been extensively studied for tumor classification, their indirect and subjective interpretation obstructs pathologists from comprehending the model and discerning the morphological features accountable for classifications. In this study, we introduce a new approach utilizing Style Generative Adversarial Networks, which enables a direct interpretation of deep learning models to detect significant morphological characteristics within datasets representing patients with deficient mismatch repair/microsatellite instability-high gastric cancer. Our approach effectively identifies distinct morphological features crucial for tumor classification, offering valuable insights for pathologists to enhance diagnostic accuracy and foster professional growth. [Display omitted] •MMRNet can automatically predict MMR/MSI status from gastric cancer H&E-stained WSIs•A mapping approach MMRMapping can efficiently interpret MMRNet•The interpretable method can aid the growth of young pathologists Pathology; Diagnostics; Cancer; Machine learning