A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging.

Atif Tahir M

Jha D(1), Ali S(2), Hicks S(3), Thambawita V(3), Borgli H(4), Smedsrud PH(5), de Lange T(6), Pogorelov K(7), Wang X(8), Harzig P(9), Tran MT(10), Meng W(11), Hoang TH(10), Dias D(12), Ko TH(13), Agrawal T(14), Ostroukhova O(15), Khan Z(16), Atif Tahir M(16), Liu Y(17), Chang Y(18), Kirkerød M(7), Johansen D(19), Lux M(20), Johansen HD(19), Riegler MA(21), Halvorsen P(3).
Author information:
(1)SimulaMet, Oslo, Norway; UiT The Arctic University of Norway, Tromsø, Norway. Electronic address: [Email]
(2)Department of Engineering Science, University of Oxford, Oxford, UK; Oxford NIHR Biomedical Research Centre, Oxford, UK.
(3)SimulaMet, Oslo, Norway; Oslo Metropolitan University, Oslo, Norway.
(4)SimulaMet, Oslo, Norway; University of Oslo, Oslo, Norway.
(5)SimulaMet, Oslo, Norway; University of Oslo, Oslo, Norway; Augere Medical AS, Oslo, Norway.
(6)SimulaMet, Oslo, Norway; Augere Medical AS, Oslo, Norway; Sahlgrenska University Hospital, Molndal, Sweden; Bærum Hospital, Vestre Viken, Oslo, Norway.
(7)Simula Research Laboratory, Oslo, Norway.
(8)DeepBlue Technology, Shanghai, China.
(9)University of Augsburg, Augsburg, Germany.
(10)University of Science, VNU-HCM, Vietnam.
(11)ZhengZhou University, ZhengZhou, China.
(12)University of Campinas, Brazil.
(13)The University of Hong Kong, Hong Kong.
(14)University of Southern California, Los Angeles, USA.
(15)Research Institute of Multiprocessor Computation Systems, Russia.
(16)School of Computer Science, National University of Computer and Emerging Sciences, Karachi Campus, Pakistan.
(17)Hong Kong Baptist University, Hong Kong.
(18)Beijing University of Posts and Telecom., China.
(19)UiT The Arctic University of Norway, Tromsø, Norway.
(20)Alpen-Adria-Universität Klagenfurt, Klagenfurt, Austria.
(21)SimulaMet, Oslo, Norway.

https://dx.doi.org/10.1016/j.media.2021.102007

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.