Attention Consistent Longitudinal Medical Visual Question Answering Guided by Vision Foundation Models
arXiv:2606.06534v1 Announce Type: cross Abstract: Longitudinal medical visual question answering (VQA) requires reasoning about anatomical differences between an image of a current time point and an image of a referred time point. We propose an attention-guided encoder-decoder for this task with chest X-rays. Instead of conventional direct contrast, we propose to include a lightweight affine registration module to reduce nuisance motion by co-registering the current image to the reference image with a small registration regularizer. The registered image pair is fed into the image encoder, foll
The continuous advancements in Vision Foundation Models and their application to specific, complex tasks like longitudinal medical VQA are pushing the boundaries of AI capabilities in healthcare.
This development demonstrates progress towards more accurate and automated medical image analysis, which can improve diagnostic efficiency and reduce human error in clinical settings.
The ability to more precisely compare medical images over time, even with 'nuisance motion,' enhances the reliability of AI-guided diagnostic tools.
- · Medical AI companies
- · Healthcare providers
- · Patients
- · Medical imaging manufacturers
- · Traditional diagnostic methods
- · Companies slow to adopt AI
Improved accuracy and efficiency in longitudinal medical diagnosis using chest X-rays.
Reduced workload for radiologists and potentially earlier detection of medical conditions.
Acceleration of AI integration into broader medical specialities beyond radiology, leading to new healthcare paradigms.
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Read at arXiv cs.AI