WsiCaption: Multiple Instance Generation of Pathology Reports for Gigapixel Whole-Slide Images

TL;DR

This paper introduces WsiCaption, a model that generates detailed pathology reports from gigapixel whole-slide images, supported by a large curated dataset, improving automation and accuracy in digital pathology.

Contribution

We present a new large-scale WSI-text dataset (PathText) and a multiple instance generative model (MI-Gen) for automatic pathology report generation from gigapixel images.

Findings

Our model produces reports with multiple clinical clues.

Achieves competitive performance on slide-level tasks.

Semantic report extraction surpasses previous methods in BRCA subtyping.

Abstract

Whole slide images are the foundation of digital pathology for the diagnosis and treatment of carcinomas. Writing pathology reports is laborious and error-prone for inexperienced pathologists. To reduce the workload and improve clinical automation, we investigate how to generate pathology reports given whole slide images. On the data end, we curated the largest WSI-text dataset (PathText). In specific, we collected nearly 10000 high-quality WSI-text pairs for visual-language models by recognizing and cleaning pathology reports which narrate diagnostic slides in TCGA. On the model end, we propose the multiple instance generative model (MI-Gen) which can produce pathology reports for gigapixel WSIs. We benchmark our model on the largest subset of TCGA-PathoText. Experimental results show our model can generate pathology reports which contain multiple clinical clues and achieve competitive performance on certain slide-level tasks. We observe that simple semantic extraction from the pathology reports can achieve the best performance (0.838 of F1 score) on BRCA subtyping surpassing previous state-of-the-art approaches. Our collected dataset and related code are available.