Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge.

Autor: Ali S; School of Computing, Faculty of Engineering and Physical Sciences, University of Leeds, Leeds, LS2 9JT, UK. s.s.ali@leeds.ac.uk.; Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, OX3 7DQ, UK. s.s.ali@leeds.ac.uk.; Oxford National Institute for Health Research Biomedical Research Centre, Oxford, OX4 2PG, UK. s.s.ali@leeds.ac.uk., Ghatwary N; Computer Engineering Department, Arab Academy for Science and Technology, Smart Village, Giza, Egypt., Jha D; SimulaMet, 0167, Oslo, Norway.; Department of Computer Science, UiT The Arctic University of Norway, Hansine Hansens veg 18, 9019, Tromsø, Norway., Isik-Polat E; Graduate School of Informatics, Middle East Technical University, 06800, Ankara, Turkey., Polat G; Graduate School of Informatics, Middle East Technical University, 06800, Ankara, Turkey., Yang C; City University of Hong Kong, Kowloon, Hong Kong., Li W; City University of Hong Kong, Kowloon, Hong Kong., Galdran A; BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08018, Barcelona, Spain., Ballester MG; BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08018, Barcelona, Spain.; ICREA, Barcelona, Spain., Thambawita V; SimulaMet, 0167, Oslo, Norway., Hicks S; SimulaMet, 0167, Oslo, Norway., Poudel S; Department of IT Convergence Engineering, Gachon University, Seongnam, 13120, Republic of Korea., Lee SW; Department of IT Convergence Engineering, Gachon University, Seongnam, 13120, Republic of Korea., Jin Z; College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China., Gan T; College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China., Yu C; Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China., Yan J; Department of Automation, Tsinghua University, Beijing, 100084, China., Yeo D; Smart Sensing and Diagnosis Research Division, Korea Atomic Energy Research Institute, Taejon, 34057, Republic of Korea., Lee H; Daegu-Gyeongbuk Medical Innovation Foundation, Medical Device Development Center, Taegu, 427724, Republic of Korea., Tomar NK; NepAL Applied Mathematics and Informatics Institute for Research (NAAMII), Kathmandu, Nepal., Haithami M; Computer Science Department, University of Nottingham, Malaysia Campus, 43500, Semenyih, Malaysia., Ahmed A; Computer Science, Edge Hill University, Lancashire, United Kingdom., Riegler MA; SimulaMet, 0167, Oslo, Norway.; Department of Computer Science, UiT The Arctic University of Norway, Hansine Hansens veg 18, 9019, Tromsø, Norway., Daul C; CRAN UMR 7039, Université de Lorraine and CNRS, 54500, Vandœuvre-Lès-Nancy, France., Halvorsen P; SimulaMet, 0167, Oslo, Norway.; Oslo Metropolitan University, Pilestredet 46, 0167, Oslo, Norway., Rittscher J; Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, OX3 7DQ, UK., Salem OE; Faculty of Medicine, University of Alexandria, Alexandria, 21131, Egypt., Lamarque D; Université de Versailles St-Quentin en Yvelines, Hôpital Ambroise Paré, 9 Av. Charles de Gaulle, 92100, Boulogne-Billancourt, France., Cannizzaro R; CRO Centro Riferimento Oncologico IRCCS Aviano Italy, Via Franco Gallini, 2, 33081, Aviano, PN, Italy., Realdon S; CRO Centro Riferimento Oncologico IRCCS Aviano Italy, Via Franco Gallini, 2, 33081, Aviano, PN, Italy.; Veneto Institute of Oncology IOV-IRCCS, Via Gattamelata, 64, 35128, Padua, Italy., de Lange T; Medical Department, Sahlgrenska University Hospital-Mölndal, Blå stråket 5, 413 45, Göteborg, Sweden.; Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, 41345, Göteborg, Sweden.; Augere Medical, Nedre Vaskegang 6, Oslo, 0186, Norway., East JE; Oxford National Institute for Health Research Biomedical Research Centre, Oxford, OX4 2PG, UK.; Translational Gastroenterology Unit, Nuffield Department of Medicine, Experimental Medicine Division, John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DU, UK.

Publikováno v: Scientific reports [Sci Rep] 2024 Jan 23; Vol. 14 (1), pp. 2032. Date of Electronic Publication: 2024 Jan 23.

Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge

Autor: Ali, S, Ghatwary, N, Jha, D, Isik-Polat, E, Polat, G, Yang, C, Li, W, Galdran, A, Ballester, M-ÁG, Thambawita, V, Hicks, S, Poudel, S, Lee, S-W, Jin, Z, Gan, T, Yu, C, Yan, J, Yeo, D, Lee, H, Tomar, NK, Haithmi, M, Ahmed, A, Riegler, MA, Daul, C, Halvorsen, P, Rittscher, J, Salem, OE, Lamarque, D, Cannizzaro, R, Realdon, S, Lange, TD, East, JE

Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, location, and surface largely affect identification, localisation, and characterisation. Moreover, colonoscopic surveillance and removal of polyps

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ff0d7ae9dd53f81ed4c2a112ccf20872
http://arxiv.org/abs/2202.12031

TGANet: Text-guided attention for improved polyp segmentation.

Autor: Tomar NK; NepAL Applied Mathematics and Informatics Institute for Research (NAAMII), Kathmandu, Nepal., Jha D; Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, USA., Bagci U; Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, USA., Ali S; Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, OX3 7DQ, Oxford, UK.; NIHR Oxford Biomedical Research Centre, Oxford, UK.

Publikováno v: Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention [Med Image Comput Comput Assist Interv] 2022 Sep; Vol. 13433, pp. 151-160. Date of Electronic Publication: 2022 Sep 16.

Automatic Polyp Segmentation with Multiple Kernel Dilated Convolution Network.

Autor: Tomar NK; School of Computer and Information Sciences, Indira Gandhi National Open University., Srivastava A; Computer Vision and Pattern Recognition Unit, Indian Statistical Institute., Bagci U; Machine and Hybrid Intelligence Lab, Department of Radiology, Northwestern University, USA., Jha D; Machine and Hybrid Intelligence Lab, Department of Radiology, Northwestern University, USA.

Publikováno v: Proceedings. IEEE International Symposium on Computer-Based Medical Systems [Proc IEEE Int Symp Comput Based Med Syst] 2022 Jul; Vol. 2022, pp. 317-322. Date of Electronic Publication: 2022 Aug 31.

DLGRAFE-Net: A double loss guided residual attention and feature enhancement network for polyp segmentation.

Autor: Liu, Jianuo^1,2 (AUTHOR), Mu, Juncheng² (AUTHOR), Sun, Haoran² (AUTHOR), Dai, Chenxu² (AUTHOR), Ji, Zhanlin^1,3 (AUTHOR) zhanlin.ji@gmail.com, Ganchev, Ivan^3,4,5 (AUTHOR) ivan.ganchev@ul.ie

Publikováno v: PLoS ONE. 9/12/2024, Vol. 19 Issue 9, p1-18. 18p.