On the Challenges and Perspectives of Foundation Models for Medical Image Analysis
It discusses opportunities for enhancing medical imaging tasks, but is incremental as it reviews existing concepts without presenting new results.
The paper examines the potential of foundation models in medical image analysis to improve accuracy and efficiency in diagnosis and treatment, while addressing challenges like data requirements and privacy.
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.