For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
2
views
27
references
 Top references
cited by
1
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      934
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Colorectal image analysis for polyp diagnosis

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Colorectal polyp is an important early manifestation of colorectal cancer, which is significant for the prevention of colorectal cancer. Despite timely detection and manual intervention of colorectal polyps can reduce their chances of becoming cancerous, most existing methods ignore the uncertainties and location problems of polyps, causing a degradation in detection performance. To address these problems, in this paper, we propose a novel colorectal image analysis method for polyp diagnosis via PAM-Net. Specifically, a parallel attention module is designed to enhance the analysis of colorectal polyp images for improving the certainties of polyps. In addition, our method introduces the GWD loss to enhance the accuracy of polyp diagnosis from the perspective of polyp location. Extensive experimental results demonstrate the effectiveness of the proposed method compared with the SOTA baselines. This study enhances the performance of polyp detection accuracy and contributes to polyp detection in clinical medicine.

          Related collections

          Most cited references27

          • Record: found
          • Abstract: not found
          • Book Chapter: not found

          CBAM: Convolutional Block Attention Module

          Sanghyun Woo, Jongchan Park, Joon-Young Lee … (2018)
          Article 0 comments Cited 846 times – based on 0 reviews
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Performance of artificial intelligence in colonoscopy for adenoma and polyp detection: a systematic review and meta-analysis

            Cesare Hassan, Marco Spadaccini, Andrea Iannone … (2021)
            One-fourth of colorectal neoplasia are missed at screening colonoscopy, representing the main cause of interval colorectal cancer. Deep learning systems with real-time computer-aided polyp detection (CADe) showed high accuracy in artificial settings, and preliminary randomized controlled trials (RCTs) reported favorable outcomes in the clinical setting. The aim of this meta-analysis was to summarize available RCTs on the performance of CADe systems in colorectal neoplasia detection.
            Article 0 comments Cited 139 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information.

              Suryakanth R Gurudu, Jianming Liang, Nima Tajbakhsh (2016)
              This paper presents the culmination of our research in designing a system for computer-aided detection (CAD) of polyps in colonoscopy videos. Our system is based on a hybrid context-shape approach, which utilizes context information to remove non-polyp structures and shape information to reliably localize polyps. Specifically, given a colonoscopy image, we first obtain a crude edge map. Second, we remove non-polyp edges from the edge map using our unique feature extraction and edge classification scheme. Third, we localize polyp candidates with probabilistic confidence scores in the refined edge maps using our novel voting scheme. The suggested CAD system has been tested using two public polyp databases, CVC-ColonDB, containing 300 colonoscopy images with a total of 300 polyp instances from 15 unique polyps, and ASU-Mayo database, which is our collection of colonoscopy videos containing 19,400 frames and a total of 5,200 polyp instances from 10 unique polyps. We have evaluated our system using free-response receiver operating characteristic (FROC) analysis. At 0.1 false positives per frame, our system achieves a sensitivity of 88.0% for CVC-ColonDB and a sensitivity of 48% for the ASU-Mayo database. In addition, we have evaluated our system using a new detection latency analysis where latency is defined as the time from the first appearance of a polyp in the colonoscopy video to the time of its first detection by our system. At 0.05 false positives per frame, our system yields a polyp detection latency of 0.3 seconds.
              Article 0 comments Cited 128 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Contributors
                Peng-Cheng Zhu: URI : http://loop.frontiersin.org/people/2568736/overviewRole: Role: Role: Role: Role:
                Jing-Jing Wan: Role: Role: Role: Role: Role:
                Wei Shao: URI : http://loop.frontiersin.org/people/1901081/overviewRole: Role: Role: Role:
                Xian-Chun Meng: Role: Role: Role:
                Bo-Lun Chen: URI : http://loop.frontiersin.org/people/2515737/overviewRole: Role: Role: Role: Role:
                Journal
                Journal ID (nlm-ta): Front Comput Neurosci
                Journal ID (iso-abbrev): Front Comput Neurosci
                Journal ID (publisher-id): Front. Comput. Neurosci.
                Title: Frontiers in Computational Neuroscience
                Publisher: Frontiers Media S.A.
                ISSN (Electronic): 1662-5188
                Publication date (Electronic): 09 February 2024
                Publication date Collection: 2024
                Volume: 18
                Electronic Location Identifier: 1356447
                Affiliations
                [1] 1Faculty of Computer and Software Engineering, Huaiyin Institute of Technology , Huaian, China
                [2] 2Department of Gastroenterology, The Second People's Hospital of Huai'an, The Affiliated Huai'an Hospital of Xuzhou Medical University, Huaian , Jiangsu, China
                [3] 3Nanjing University of Aeronautics and Astronautics Shenzhen Research Institute , Shenzhen, China
                [4] 4Department of Physics, University of Fribourg , Fribourg, Switzerland
                Author notes

                Edited by: Yunhao Yuan, Yangzhou University, China

                Reviewed by: Mingliang Wang, Nanjing University of Information Science and Technology, China

                Guoqing Zhang, Nanjing University of Information Science and Technology, China

                Yinghuan Shi, Nanjing University, China

                *Correspondence: Peng-Cheng Zhu zhupc2023@ 123456hyit.edu.cn

                †These authors have contributed equally to this work and share first authorship

                Article
                DOI: 10.3389/fncom.2024.1356447
                PMC ID: 10884282
                PubMed ID: 38404511
                SO-VID: 514827e6-2b89-4b93-bd41-7fc12ea80d11
                Copyright © Copyright © 2024 Zhu, Wan, Shao, Meng and Chen.
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                Date received : 15 December 2023
                Date accepted : 05 January 2024
                Page count
                Figures: 9, Tables: 2, Equations: 10, References: 28, Pages: 11, Words: 6764
                Funding
                Funded by: National Natural Science Foundation of China, doi 10.13039/501100001809;
                Funded by: Natural Science Foundation of Jiangsu Province, doi 10.13039/501100004608;
                Funded by: Natural Science Foundation of Huaian Municipality, doi 10.13039/100020740;
                Funded by: Shenzhen Science and Technology Innovation Program, doi 10.13039/501100017610;
                The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was supported in part by the National Natural Science Foundation of China under grant No. 82302310, Natural Science Foundation of Jiangsu Province under contracts BK20160428, Natural Science Foundation of Education Department of Jiangsu Province under contract 20KJA520008, Natural Science Foundation of Huaian under contracts HAB201934, and the Shenzhen Science and Technology Program under Grant JCYJ20220530172403008.
                Categories
                Subject: Neuroscience
                Subject: Original Research

                ScienceOpen disciplines: Neurosciences
                Keywords: medical data mining,medical intelligence,colorectal cancer,polyp diagnosis,attention mechanism,medical image detection
                Data availability:
                ScienceOpen disciplines: Neurosciences
                Keywords: medical data mining, medical intelligence, colorectal cancer, polyp diagnosis, attention mechanism, medical image detection

                Comments

                Comment on this article