On the Challenges and Perspectives of Foundation Models for Medical Image Analysis
By Shaoting Zhang, Dimitris N. Metaxas. Medical Image Anal., 2023
314 citations recorded by the source corpus.
This article discusses the opportunities, applications and future directions of large-scale pretrained models, i.e., foundation models, which promise to significantly improve the analysis of 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 dependence on large amounts of labeled data, preserve the privacy and confidentiality of patient data. Specifically, we illustrate the "spectrum" of medical foundation models, ranging from general imaging models, modality-specific models, to organ/task-specific models, and highlight 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.
Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/fed150a219f9c31bdb4920e615c7c9264c634736
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In Computer Vision and Pattern Recognition
Citations in the corpus
- Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directions (2024, background): “Foundation models have also shown strong potential in solving a wide range of downstream tasks for medical image analysis and help to accelerate the development of accurate and robust models [35, 36, 37].”