González-Bueno Puyal, Juana;
Brandao, Patrick;
Ahmad, Omer F;
Bhatia, Kanwal K;
Toth, Daniel;
Kader, Rawen;
Lovat, Laurence;
... Stoyanov, Danail; + view all
(2023)
Spatio-temporal classification for polyp diagnosis.
Biomedical Optics Express
, 14
(2)
pp. 593-607.
10.1364/boe.473446.
Preview |
Text
boe-14-2-593.pdf - Published Version Download (6MB) | Preview |
Abstract
Colonoscopy remains the gold standard investigation for colorectal cancer screening as it offers the opportunity to both detect and resect pre-cancerous polyps. Computer-aided polyp characterisation can determine which polyps need polypectomy and recent deep learning-based approaches have shown promising results as clinical decision support tools. Yet polyp appearance during a procedure can vary, making automatic predictions unstable. In this paper, we investigate the use of spatio-temporal information to improve the performance of lesions classification as adenoma or non-adenoma. Two methods are implemented showing an increase in performance and robustness during extensive experiments both on internal and openly available benchmark datasets.
Archive Staff Only
 |
View Item |
