Summary
This paper investigates vision-language transfer learning for automated analysis of CT enterography, a key imaging modality for inflammatory bowel disease (IBD). It explores how different representation geometries, such as mean versus attention pooling of slice embeddings, impact task performance. The study found that mean pooling improves categorical disease assessment accuracy, while attention pooling is more effective for other aspects of analysis.
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[Paper] Ruka-v2: Tendon Driven Open-Source Dexterous Hand with Wrist and Abduction for Robot Learning
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