Medico Multimedia Task at MediaEval 2020: Automatic Polyp Segmentation

• URL: http://arxiv.org/abs/2012.15244v1
• Date: Wed, 30 Dec 2020 17:47:38 GMT
• Title: Medico Multimedia Task at MediaEval 2020: Automatic Polyp Segmentation
• Authors: Debesh Jha, Steven A. Hicks, Krister Emanuelsen, H{\aa}vard Johansen, Dag Johansen, Thomas de Lange, Michael A. Riegler, P{\aa}l Halvorsen
• Abstract summary: Support via an automated computer-aided diagnosis system could be one of the potential solutions for the overlooked polyps. In this paper, we introduce the 2020 Medico challenge, describe the task and evaluation metrics, and discuss the necessity of organizing the Medico challenge.
• Score: 0.6125117548653111
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Colorectal cancer is the third most common cause of cancer worldwide. According to Global cancer statistics 2018, the incidence of colorectal cancer is increasing in both developing and developed countries. Early detection of colon anomalies such as polyps is important for cancer prevention, and automatic polyp segmentation can play a crucial role for this. Regardless of the recent advancement in early detection and treatment options, the estimated polyp miss rate is still around 20\%. Support via an automated computer-aided diagnosis system could be one of the potential solutions for the overlooked polyps. Such detection systems can help low-cost design solutions and save doctors time, which they could for example use to perform more patient examinations. In this paper, we introduce the 2020 Medico challenge, provide some information on related work and the dataset, describe the task and evaluation metrics, and discuss the necessity of organizing the Medico challenge.

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