RARE disease detection from Capsule Endoscopic Videos based on Vision Transformers
arXiv cs.CV / 3/20/2026
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Key Points
- The study applies a Vision Transformer (ViT) based network, fine-tuned for multi-label classification on capsule endoscopic videos, using batch size 16 and 224x224 input patches.
- It defines 17 labels, covering anatomical regions (mouth, esophagus, stomach, small intestine, colon, z-line, pylorus, ileocecal valve) and findings (active bleeding, angiectasia, blood, erosion, erythema, hematin, lymphangioectasis, polyp, ulcer), and tests on three videos from Gastro Competition.
- On the test set of three videos, the reported mean average precision is 0.0205 at IoU 0.5 and 0.0196 at IoU 0.95, indicating very limited performance for this task so far.
- The work demonstrates the feasibility of applying transformers to capsule endoscopic video analysis but underscores the need for better datasets and architectures to improve rare-disease detection in medical imaging.
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