Breast invasive ductal carcinoma classification on whole slide images with weakly-supervised and transfer learning

Abstract

AbstractInvasive ductal carcinoma (IDC) is the most common form of breast cancer. For the non-operative diagnosis of breast carcinoma, core needle biopsy has been widely used in recent years which allows evaluation of both cytologic and tissue architectural features; so that it can provide a definitive diagnosis between IDC and benign lesion (e.g., fibroadenoma). Histopathological diagnosis based on core needle biopsy specimens is currently the cost effective method; therefore, it is an area that could benefit from AI-based tools to aid pathologists in their pathological diagnosis workflows. In this paper, we trained an Invasive Ductal Carcinoma (IDC) Whole Slide Image (WSI) classification model using transfer learning and weakly-supervised learning. We evaluated the model on a core needle biopsy (n=522) test set as well as three surgical test sets (n=1129) obtaining ROC AUCs in the range of 0.95-0.98.