Deep learning for detection and segmentation of artefact and disease
instances in gastrointestinal endoscopy
- URL: http://arxiv.org/abs/2010.06034v2
- Date: Wed, 17 Feb 2021 17:49:32 GMT
- Title: Deep learning for detection and segmentation of artefact and disease
instances in gastrointestinal endoscopy
- Authors: Sharib Ali, Mariia Dmitrieva, Noha Ghatwary, Sophia Bano, Gorkem
Polat, Alptekin Temizel, Adrian Krenzer, Amar Hekalo, Yun Bo Guo, Bogdan
Matuszewski, Mourad Gridach, Irina Voiculescu, Vishnusai Yoganand, Arnav
Chavan, Aryan Raj, Nhan T. Nguyen, Dat Q. Tran, Le Duy Huynh, Nicolas Boutry,
Shahadate Rezvy, Haijian Chen, Yoon Ho Choi, Anand Subramanian, Velmurugan
Balasubramanian, Xiaohong W. Gao, Hongyu Hu, Yusheng Liao, Danail Stoyanov,
Christian Daul, Stefano Realdon, Renato Cannizzaro, Dominique Lamarque, Terry
Tran-Nguyen, Adam Bailey, Barbara Braden, James East and Jens Rittscher
- Abstract summary: The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems.
There are several core challenges often faced by endoscopists, mainly: 1) presence of multi-class artefacts that hinder their visual interpretation, and 2) difficulty in identifying subtle precancerous precursors and cancer abnormalities.
EndoCV 2020 challenges are designed to address research questions in these remits.
- Score: 7.840459682652335
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing
initiative to address eminent problems in developing reliable computer aided
detection and diagnosis endoscopy systems and suggest a pathway for clinical
translation of technologies. Whilst endoscopy is a widely used diagnostic and
treatment tool for hollow-organs, there are several core challenges often faced
by endoscopists, mainly: 1) presence of multi-class artefacts that hinder their
visual interpretation, and 2) difficulty in identifying subtle precancerous
precursors and cancer abnormalities. Artefacts often affect the robustness of
deep learning methods applied to the gastrointestinal tract organs as they can
be confused with tissue of interest. EndoCV2020 challenges are designed to
address research questions in these remits. In this paper, we present a summary
of methods developed by the top 17 teams and provide an objective comparison of
state-of-the-art methods and methods designed by the participants for two
sub-challenges: i) artefact detection and segmentation (EAD2020), and ii)
disease detection and segmentation (EDD2020). Multi-center, multi-organ,
multi-class, and multi-modal clinical endoscopy datasets were compiled for both
EAD2020 and EDD2020 sub-challenges. The out-of-sample generalization ability of
detection algorithms was also evaluated. Whilst most teams focused on accuracy
improvements, only a few methods hold credibility for clinical usability. The
best performing teams provided solutions to tackle class imbalance, and
variabilities in size, origin, modality and occurrences by exploring data
augmentation, data fusion, and optimal class thresholding techniques.
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