End-to-end metastasis detection of breast cancer from histopathology whole slide images. (December 2022) - Khaliliboroujeni, Sepideh
Abstract: Worldwide breast cancer is one of the most frequent and mortal diseases across women. Early, accurate metastasis cancer detection is a significant factor in raising the survival rate among patients. Diverse Computer-Aided Diagnostic (CAD) systems applying medical imaging modalities, have been designed for breast cancer detection. The impact of deep learning in improving CAD systems' performance is undeniable. Among all of the medical image modalities, histopathology (HP) images consist of richer phenotypic details and help keep track of cancer metastasis. Nonetheless, metastasis detection in whole slide images (WSIs) is still problematic because of the enormous size of these images and the massive cost of labelling them. In this paper, we develop a reliable, fast and accurate CAD system for metastasis detection in breast cancer while applying only a small amount of annotated data with lower resolution. This saves considerable time and cost. Unlike other works which apply patch classification for tumor detection, we employ the benefits of attention modules adding to regression and classification, to extract tumor parts simultaneously. Then, we use dense prediction for mask generation and identify individual metastases in WSIs. Experimental outcomes demonstrate the efficiency of our method. It provides more accurate results than other methods that apply the total dataset. The proposed method is about seven times faster than an expert pathologist, while producing even more accurate results than an expert pathologist in tumor detection. Highlights: We propose a CAD system for metastasis detection of breast cancer in lymph nodes from histopathology Whole Slide Images. We propose to adopt spatial and channel attentions, patch classification and tumor ratio prediction to extract density maps. We assess classification and regression sub-branches rather than applying pixel loss that requires pixel-level annotations. Our approach is more accurate and significantly faster than the approaches for the CAMELYON16 challenge.