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Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
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RadAgent doesn't just give you the answer; it shows its work, offering clinicians a transparent, step-by-step reasoning trace for AI-generated CT reports.
Representing complex 3D biomedical graphs as learned tokens unlocks generative modeling and efficient analysis of anatomical structures.
Ditch fixed-size 3D blocks: SigVLP uses rotary embeddings to let vision-language models handle CT volumes with variable slice counts, unlocking better pre-training.
VariViT lets you train vision transformers on variable-sized images without resizing, boosting accuracy on medical imaging tasks by better preserving irregularly shaped structures.
