A New Era in Computational Pathology: A Survey on Foundation and Vision-Language Models

TL;DR

This survey reviews recent innovations in foundation and vision-language models in computational pathology, highlighting their potential to revolutionize diagnostic workflows and improve decision-making processes.

Contribution

It provides a comprehensive overview of recent developments, tools, datasets, and training schemes for FMs and VLMs in computational pathology.

Findings

FMs enable adaptable representations for various tasks.

VLMs incorporate natural language reports for richer analysis.

Current trends suggest a future revolution in CPath with these models.

Abstract

Recent advances in deep learning have completely transformed the domain of computational pathology (CPath). More specifically, it has altered the diagnostic workflow of pathologists by integrating foundation models (FMs) and vision-language models (VLMs) in their assessment and decision-making process. The limitations of existing deep learning approaches in CPath can be overcome by FMs through learning a representation space that can be adapted to a wide variety of downstream tasks without explicit supervision. Deploying VLMs allow pathology reports written in natural language be used as rich semantic information sources to improve existing models as well as generate predictions in natural language form. In this survey, a holistic and systematic overview of recent innovations in FMs and VLMs in CPath is presented. Furthermore, the tools, datasets and training schemes for these models are summarized in addition to categorizing them into distinct groups. This extensive survey highlights the current trends in CPath and its possible revolution through the use of FMs and VLMs in the future.