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Current AI models miss critical tumor detections in underrepresented demographics, revealing a hidden bias that could compromise patient outcomes.
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.
