Explanation and Application by Using Attention in Digital Pathology

Abstract

Digital images in the field of pathology are known as whole slide images (WSIs), which are high-resolution scans of glass specimens captured by WSI scanners. Due to their large size, WSIs cannot be directly input into machine learning models; instead, small image patches extracted from WSIs are typically used as input. However, annotating tumors within these images requires significant effort from pathologists, making it difficult to assign accurate labels to each patch. As a result, it is common in digital pathology to treat the problem as one in which a single class label is assigned to an entire WSI. In such weakly supervised settings, multiple instance learning (MIL) has proven effective. In particular, attention-based MIL (ABMIL) has become a widely adopted model, as it allows for visualization of attention weights to highlight regions within a WSI that contribute to the model’s decision. This makes ABMIL a promising approach for explainable AI in digital pathology. This paper provides an overview of ABMIL and its applications in the context of explainable AI.