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University of Zurich
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Achieving multi-class segmentation and classification of intracranial aneurysms across diverse imaging modalities could revolutionize treatment planning and risk assessment.
Automated segmentation of all blood vessels in CT images could revolutionize cardiovascular diagnostics by enabling systemic health assessments rather than isolated analyses.
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.
