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A comprehensive ana...
A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging
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Jha, Debesh (author)
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Ali, Sharib (author)
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Hicks, Steven (author)
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Thambawita, Vajira (author)
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Borgli, Hanna (author)
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Smedsrud, Pia H. (author)
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- de Lange, Thomas, 1960 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine
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Pogorelov, Konstantin (author)
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Wang, Xiaowei (author)
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Harzig, Philipp (author)
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Tran, Minh Triet (author)
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Meng, Wenhua (author)
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Hoang, Trung Hieu (author)
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Dias, Danielle (author)
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Ko, Tobey H. (author)
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Agrawal, Taruna (author)
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Ostroukhova, Olga (author)
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Khan, Zeshan (author)
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Atif Tahir, Muhammad (author)
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Liu, Yang (author)
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Chang, Yuan (author)
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Kirkerød, Mathias (author)
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Johansen, Dag (author)
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Lux, Mathias (author)
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Johansen, Håvard D. (author)
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Riegler, Michael A. (author)
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Halvorsen, Pål (author)
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- 2021
- 2021
- English.
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In: Medical Image Analysis. - 1361-8415 .- 1361-8423. ; 70
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Abstract
Subject headings
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- Gastrointestinal (GI) endoscopy has been an active field of research motivated by the large number of highly lethal GI cancers. Early GI cancer precursors are often missed during the endoscopic surveillance. The high missed rate of such abnormalities during endoscopy is thus a critical bottleneck. Lack of attentiveness due to tiring procedures, and requirement of training are few contributing factors. An automatic GI disease classification system can help reduce such risks by flagging suspicious frames and lesions. GI endoscopy consists of several multi-organ surveillance, therefore, there is need to develop methods that can generalize to various endoscopic findings. In this realm, we present a comprehensive analysis of the Medico GI challenges: Medical Multimedia Task at MediaEval 2017, Medico Multimedia Task at MediaEval 2018, and BioMedia ACM MM Grand Challenge 2019. These challenges are initiative to set-up a benchmark for different computer vision methods applied to the multi-class endoscopic images and promote to build new approaches that could reliably be used in clinics. We report the performance of 21 participating teams over a period of three consecutive years and provide a detailed analysis of the methods used by the participants, highlighting the challenges and shortcomings of the current approaches and dissect their credibility for the use in clinical settings. Our analysis revealed that the participants achieved an improvement on maximum Mathew correlation coefficient (MCC) from 82.68% in 2017 to 93.98% in 2018 and 95.20% in 2019 challenges, and a significant increase in computational speed over consecutive years.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik (hsv//swe)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering (hsv//eng)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine (hsv//eng)
Keyword
- Artificial intelligence
- BioMedia 2019 grand challenge
- Computer-aided detection and diagnosis
- Gastrointestinal endoscopy challenges
- Medical imaging
- Medico Task 2017
- Medico Task 2018
Publication and Content Type
- ref (subject category)
- art (subject category)
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- By the author/editor
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Jha, Debesh
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Ali, Sharib
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Hicks, Steven
-
Thambawita, Vaji ...
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Borgli, Hanna
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Smedsrud, Pia H.
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show more...
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de Lange, Thomas ...
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Pogorelov, Konst ...
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Wang, Xiaowei
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Harzig, Philipp
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Tran, Minh Triet
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Meng, Wenhua
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Hoang, Trung Hie ...
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Dias, Danielle
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Ko, Tobey H.
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Agrawal, Taruna
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Ostroukhova, Olg ...
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Khan, Zeshan
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Atif Tahir, Muha ...
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Liu, Yang
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Chang, Yuan
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Kirkerød, Mathia ...
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Johansen, Dag
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Lux, Mathias
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Johansen, Håvard ...
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Riegler, Michael ...
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Halvorsen, Pål
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show less...
- About the subject
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- ENGINEERING AND TECHNOLOGY
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ENGINEERING AND ...
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and Medical Engineer ...
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- MEDICAL AND HEALTH SCIENCES
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MEDICAL AND HEAL ...
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and Clinical Medicin ...
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Medical Image An ...
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University of Gothenburg