RARE disease detection from Capsule Endoscopic Videos based on Vision Transformers

Fuente: arXiv
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Hauptverfasser: Gao, X., Chien, C., Liu, G., Manullang, A.
Format: Preprint
Veröffentlicht: 2026
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author Gao, X.
Chien, C.
Liu, G.
Manullang, A.
author_facet Gao, X.
Chien, C.
Liu, G.
Manullang, A.
contents This work is corresponding to the Gastro Competition for multi-label classification from capsule endoscopic videos (CEV). Deep learning network based on Transformers are fined-tune for this task. The based online mode is Google Vision Transformer (ViT) batch16 with 224 x 224 resolutions. In total, 17 labels are classified, which are mouth, esophagus, stomach, small intestine, colon, z-line, pylorus, ileocecal valve, active bleeding, angiectasia, blood, erosion, erythema, hematin, lymphangioectasis, polyp, and ulcer. For test dataset of three videos, the overall mAP @0.5 is 0.0205 whereas the overall mAP @0.95 is 0.0196.
format Preprint
id arxiv_https___arxiv_org_abs_2603_18045
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle RARE disease detection from Capsule Endoscopic Videos based on Vision Transformers
Gao, X.
Chien, C.
Liu, G.
Manullang, A.
Computer Vision and Pattern Recognition
This work is corresponding to the Gastro Competition for multi-label classification from capsule endoscopic videos (CEV). Deep learning network based on Transformers are fined-tune for this task. The based online mode is Google Vision Transformer (ViT) batch16 with 224 x 224 resolutions. In total, 17 labels are classified, which are mouth, esophagus, stomach, small intestine, colon, z-line, pylorus, ileocecal valve, active bleeding, angiectasia, blood, erosion, erythema, hematin, lymphangioectasis, polyp, and ulcer. For test dataset of three videos, the overall mAP @0.5 is 0.0205 whereas the overall mAP @0.95 is 0.0196.
title RARE disease detection from Capsule Endoscopic Videos based on Vision Transformers
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2603.18045