Downstream Analysis of Foundational Medical Vision Models for Disease Progression

• URL: http://arxiv.org/abs/2503.16842v1
• Date: Fri, 21 Mar 2025 04:27:49 GMT
• Title: Downstream Analysis of Foundational Medical Vision Models for Disease Progression
• Authors: Basar Demir, Soumitri Chattopadhyay, Thomas Hastings Greer, Boqi Chen, Marc Niethammer,
• Abstract summary: This work evaluates the ability of medical vision foundational models to predict disease progression using a simple linear probe.<n>We show that intermediate layer features of segmentation models capture structural information, while those of registration models encode knowledge of change over time.
• Score: 13.871967855022415
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Medical vision foundational models are used for a wide variety of tasks, including medical image segmentation and registration. This work evaluates the ability of these models to predict disease progression using a simple linear probe. We hypothesize that intermediate layer features of segmentation models capture structural information, while those of registration models encode knowledge of change over time. Beyond demonstrating that these features are useful for disease progression prediction, we also show that registration model features do not require spatially aligned input images. However, for segmentation models, spatial alignment is essential for optimal performance. Our findings highlight the importance of spatial alignment and the utility of foundation model features for image registration.

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