An efficient context-aware approach for whole slide image classification

Abstract

Computational pathology for gigapixel whole slide images (WSIs) at slide-level is helpful in disease diagnosis and remains challenging. We propose a context-aware approach termed WSI Inspection via Transformer (WIT) for slide-level classification via holistically modeling dependencies among patches on the WSI. WIT automatically learns feature representation of WSI by aggregating features of all image patches. We evaluate classification performance of WIT along with state-of-the-art baseline method. WIT achieved an accuracy of 82.1% (95% CI, 80.7% - 83.3%) in the detection of 32 cancer types on the TCGA dataset, 0.918 (0.910 - 0.925) in diagnosis of cancer on the CPTAC dataset and 0.882 (0.87 - 0.890) in the diagnosis of prostate cancer from needle biopsy slide, outperforming the baseline by 31.6%, 5.4% and 9.3%, respectively. WIT can pinpoint the WSI regions that are most influential for its decision. WIT represents a new paradigm for computational pathology, facilitating the development of effective tools for digital pathology.