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.
Funder
Horizon 2020 Framework
Programme
Royal Academy of
Engineering
Engineering and Physical Sciences
Research Council
Wellcome / EPSRC Centre for
Interventional and Surgical Sciences
Subject
Atomic and Molecular Physics, and Optics,Biotechnology
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献