Diagnostic Text-guided Representation Learning in Hierarchical Classification for Pathological Whole Slide Image

TL;DR

This paper introduces PathTree, a hierarchical representation learning method guided by pathology texts for classifying complex whole slide images, improving accuracy in multi-class cancer diagnosis tasks.

Contribution

The paper proposes PathTree, a novel hierarchical classification framework that integrates pathology text descriptions with slide images using a tree-structured encoder and slide-text similarity.

Findings

PathTree outperforms state-of-the-art methods on three challenging datasets.

It effectively models complex relationships between lesion types.

The approach provides a new perspective for deep learning in pathology image analysis.

Abstract

With the development of digital imaging in medical microscopy, artificial intelligent-based analysis of pathological whole slide images (WSIs) provides a powerful tool for cancer diagnosis. Limited by the expensive cost of pixel-level annotation, current research primarily focuses on representation learning with slide-level labels, showing success in various downstream tasks. However, given the diversity of lesion types and the complex relationships between each other, these techniques still deserve further exploration in addressing advanced pathology tasks. To this end, we introduce the concept of hierarchical pathological image classification and propose a representation learning called PathTree. PathTree considers the multi-classification of diseases as a binary tree structure. Each category is represented as a professional pathological text description, which messages information with a tree-like encoder. The interactive text features are then used to guide the aggregation of hierarchical multiple representations. PathTree uses slide-text similarity to obtain probability scores and introduces two extra tree specific losses to further constrain the association between texts and slides. Through extensive experiments on three challenging hierarchical classification datasets: in-house cryosectioned lung tissue lesion identification, public prostate cancer grade assessment, and public breast cancer subtyping, our proposed PathTree is consistently competitive compared to the state-of-the-art methods and provides a new perspective on the deep learning-assisted solution for more complex WSI classification.