Abnormalities detection from wireless capsule endoscopy images based on embedding learning with triplet loss
Author:
Funder
Ministère de l’Education Nationale, de la Formation professionnelle, de l’Enseignement Supérieur et de la Recherche Scientifique
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11042-024-18391-w.pdf
Reference36 articles.
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2. Charisis VS, Katsimerou C, Hadjileontiadis LJ, Liatsos CN, Sergiadis GD (2013) Computer-aided capsule endoscopy images evaluation based on color rotation and texture features: an educational tool to physicians. In: Proceedings of the 26th IEEE international symposium on computer-based medical systems, pp 203–208. IEEE
3. Coelho P, Pereira A, Salgado M, Cunha A (2018) A deep learning approach for red lesions detection in video capsule endoscopies. In: International conference image analysis and recognition, pp 553–561. Springer
4. Bernal J, Sánchez FJ, Fernández-Esparrach G, Gil D, Rodríguez C, Vilariño F (2015) Wm-dova maps for accurate polyp highlighting in colonoscopy: validation vs. saliency maps from physicians. Comput Med Imaging Graph 43:99–111
5. Amiri Z, Hassanpour H, Beghdadi A (2022) Feature extraction for abnormality detection in capsule endoscopy images. Biomed Signal Process Control 71:103219
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