3 March 2017 Fully convolutional neural networks for polyp segmentation in colonoscopy
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Abstract
Colorectal cancer (CRC) is one of the most common and deadliest forms of cancer, accounting for nearly 10% of all forms of cancer in the world. Even though colonoscopy is considered the most effective method for screening and diagnosis, the success of the procedure is highly dependent on the operator skills and level of hand-eye coordination. In this work, we propose to adapt fully convolution neural networks (FCN), to identify and segment polyps in colonoscopy images. We converted three established networks into a fully convolution architecture and fine-tuned their learned representations to the polyp segmentation task. We validate our framework on the 2015 MICCAI polyp detection challenge dataset, surpassing the state-of-the-art in automated polyp detection. Our method obtained high segmentation accuracy and a detection precision and recall of 73.61% and 86.31%, respectively.
Conference Presentation
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Patrick Brandao, Patrick Brandao, Evangelos Mazomenos, Evangelos Mazomenos, Gastone Ciuti, Gastone Ciuti, Renato Caliò, Renato Caliò, Federico Bianchi, Federico Bianchi, Arianna Menciassi, Arianna Menciassi, Paolo Dario, Paolo Dario, Anastasios Koulaouzidis, Anastasios Koulaouzidis, Alberto Arezzo, Alberto Arezzo, Danail Stoyanov, Danail Stoyanov, } "Fully convolutional neural networks for polyp segmentation in colonoscopy", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101340F (3 March 2017); doi: 10.1117/12.2254361; https://doi.org/10.1117/12.2254361
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