Colonoscopy Landmark Detection Using Vision Transformers

نویسندگان

چکیده

Colonoscopy is a routine outpatient procedure used to examine the colon and rectum for any abnormalities including polyps, diverticula narrowing of structures. A significant amount clinician’s time spent in post-processing snapshots taken during colonoscopy procedure, maintaining medical records or further investigation. Automating this step can save improve efficiency process. In our work, we have collected dataset 120 videos 2416 that been annotated by experts. Further, developed novel, vision-transformer based landmark detection algorithm identifies key anatomical landmarks (the appendiceal orifice, ileocecal valve/cecum retroflexion) from colonoscopy. Our uses an adaptive gamma correction preprocessing maintain consistent brightness all images.We then use vision transformer as feature extraction backbone fully connected network classifier head categorize given frame into four classes: three non-landmark frame. We compare (ViT-B/16) with ResNet-101 ConvNext-B backbones trained similarly. report accuracy 82% on test snapshots.

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ژورنال

عنوان ژورنال: Lecture Notes in Computer Science

سال: 2022

ISSN: ['1611-3349', '0302-9743']

DOI: https://doi.org/10.1007/978-3-031-21083-9_3