Foundation models trained on massive and diverse datasets, hold immense potential to revolutionize pathology by automating image analysis and assisting pathologists in diagnosis. This presentation will delve into introducing foundation models to the audience, exploring their key characteristics, applications in digital pathology, and a specific use case for classifying tumors. We will discuss the benefits of adopting these models, including increased diagnostic accuracy and reduced analysis time. Furthermore, we will also address the challenges associated with data privacy, ethical considerations, and the need for explainable AI. By exploring the the application of foundation models in pathology, this presentation aims to spark further discussion and collaboration between AI researchers and pathologists to optimize their integration into digital pathology research and practice.

Learning Objectives

1. Have a broad understanding of foundation models in general.
2. Know and apply foundation models to digital pathology.