A New Era in Computational Pathology: A Survey on Foundation and Vision-Language Models
It addresses the need for advanced AI tools in pathology to enhance diagnostic workflows, but it is incremental as it synthesizes existing innovations rather than introducing new methods.
This survey provides a holistic overview of how foundation models and vision-language models are transforming computational pathology by overcoming limitations of existing deep learning approaches and integrating natural language reports for improved diagnostics.
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.