On the Challenges and Perspectives of Foundation Models for Medical Image Analysis

TL;DR

This paper explores the potential and challenges of large-scale foundation models in medical image analysis, emphasizing their ability to improve accuracy, reduce data needs, and protect patient privacy.

Contribution

It provides a comprehensive overview of the spectrum of medical foundation models and discusses their applications, challenges, and future directions in medical imaging.

Findings

Foundation models can enhance diagnostic accuracy.

They reduce the need for large labeled datasets.

Foundation models help preserve patient privacy.

Abstract

This article discusses the opportunities, applications and future directions of large-scale pre-trained models, i.e., foundation models, for analyzing medical images. Medical foundation models have immense potential in solving a wide range of downstream tasks, as they can help to accelerate the development of accurate and robust models, reduce the large amounts of required labeled data, preserve the privacy and confidentiality of patient data. Specifically, we illustrate the "spectrum" of medical foundation models, ranging from general vision models, modality-specific models, to organ/task-specific models, highlighting their challenges, opportunities and applications. We also discuss how foundation models can be leveraged in downstream medical tasks to enhance the accuracy and efficiency of medical image analysis, leading to more precise diagnosis and treatment decisions.