On the Challenges and Perspectives of Foundation Models for Medical
Image Analysis
- URL: http://arxiv.org/abs/2306.05705v2
- Date: Tue, 21 Nov 2023 19:24:43 GMT
- Title: On the Challenges and Perspectives of Foundation Models for Medical
Image Analysis
- Authors: Shaoting Zhang, Dimitris Metaxas
- Abstract summary: Medical foundation models have immense potential in solving a wide range of downstream tasks.
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.
- Score: 17.613533812925635
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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