Histopathological Image Classification based on Self-Supervised Vision Transformer and Weak Labels

TL;DR

This paper introduces Self-ViT-MIL, a novel self-supervised Vision Transformer-based method for classifying and localizing cancer in whole slide images using only slide-level labels, improving accuracy over existing methods.

Contribution

It is the first to integrate self-supervised ViTs into MIL for WSI analysis, eliminating the need for pixel-level annotations and enhancing performance.

Findings

Outperforms existing MIL-based methods in accuracy

Achieves higher AUC on Camelyon16 dataset

Demonstrates effective localization of cancerous regions

Abstract

Whole Slide Image (WSI) analysis is a powerful method to facilitate the diagnosis of cancer in tissue samples. Automating this diagnosis poses various issues, most notably caused by the immense image resolution and limited annotations. WSIs commonly exhibit resolutions of 100Kx100K pixels. Annotating cancerous areas in WSIs on the pixel level is prohibitively labor-intensive and requires a high level of expert knowledge. Multiple instance learning (MIL) alleviates the need for expensive pixel-level annotations. In MIL, learning is performed on slide-level labels, in which a pathologist provides information about whether a slide includes cancerous tissue. Here, we propose Self-ViT-MIL, a novel approach for classifying and localizing cancerous areas based on slide-level annotations, eliminating the need for pixel-wise annotated training data. Self-ViT- MIL is pre-trained in a self-supervised setting to learn rich feature representation without relying on any labels. The recent Vision Transformer (ViT) architecture builds the feature extractor of Self-ViT-MIL. For localizing cancerous regions, a MIL aggregator with global attention is utilized. To the best of our knowledge, Self-ViT- MIL is the first approach to introduce self-supervised ViTs in MIL-based WSI analysis tasks. We showcase the effectiveness of our approach on the common Camelyon16 dataset. Self-ViT-MIL surpasses existing state-of-the-art MIL-based approaches in terms of accuracy and area under the curve (AUC).