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A submission note on EAD 2020: Deep learning based approach for detecting artefacts in endoscopy

Vishnusai, Y and Prakash, P and Shivashankar, N (2020) A submission note on EAD 2020: Deep learning based approach for detecting artefacts in endoscopy. In: 2nd International Workshop and Challenge on Computer Vision in Endoscopy, EndoCV 2020, 3 April 2020, Iowa City; United States, pp. 30-36.

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Abstract

Deep neural network-based methods are becoming popular for disease diagnosis and treatment in Endoscopy. In this paper, we discuss our submission to Endoscopic Artefact Detection Challenge (EAD2020). The competition is part of grand challenges in Biomedical Image Analysis and consists of three sub-tasks1: i) Bounding box-based localisation of artefacts ii) Region-based segmentation of artefacts, and iii) Out of sample generalisation task. For the first sub-task, we modify the Faster R-CNN object detector by integrating a powerful backbone network and a feature pyramidal module. For the second sub-task, we implemented a U-Net based autoencoder with a modified loss function to construct the semantic channels. For the third sub-task, we used ensembling techniques along with a data-augmentation technique inspired by RandAugment to boost the generalisation performance. We report a Scored of 0.1869 ± 0.1076 for the first task, sscore of 0.5187 with a sstd of 0.2755 for the second task and mAPg of 0.2620 and a devg of 0.0890 for the third task on the test data-set. Our method for the third task, achieves the third position on the leaderboard for the mAPg metric and also surpasses the results obtained by many methods in the previous EAD2019 challenge. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Item Type: Conference Paper
Publication: CEUR Workshop Proceedings
Publisher: CEUR-WS
Additional Information: cited By 0; Conference of 2nd International Workshop and Challenge on Computer Vision in Endoscopy, EndoCV 2020 ; Conference Date: 3 April 2020; Conference Code:159437
Keywords: Computer vision; Deep neural networks; Endoscopy; Image segmentation; Medical imaging; Object detection; Semantics; Statistical tests, Artefact detection; Back-bone network; Biomedical image analysis; Data augmentation; Disease diagnosis; Learning-based approach; Object detectors; Region-based segmentation, Deep learning
Department/Centre: Autonomous Societies / Centres > Society for Innovation and Development
Date Deposited: 04 Jan 2021 05:56
Last Modified: 04 Jan 2021 05:56
URI: http://eprints.iisc.ac.in/id/eprint/65506

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