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Öksüz, I.* ; Clough, J.R.* ; King, A.P.* ; Schnabel, J.A.*

Artefact detection in video endoscopy using retinanet and focal loss function.

In:. Aachen: RWTH, 2019. (CEUR Workshop Proc. ; 2366)
Endoscopic Artefact Detection (EAD) is a fundamental task for enabling the use of endoscopy images for diagnosis and treatment of diseases in multiple organs. Precise detection of specific artefacts such as pixel saturations, motion blur, specular reflections, bubbles and instruments is essential for high-quality frame restoration. This work describes our submission to the EAD 2019 challenge to detect bounding boxes for seven classes of artefacts in endoscopy videos. Our method is based on focal loss and Retina-net architecture with Resnet-152 backbone. We have generated a large derivative dataset by augmenting the original images with free-form deformations to prevent over-fitting. Our method reaches a mAP of 0.2719 and a IoU of 0.3456 for the detection task over all classes of artefact for 195 images. We report comparable performance for the generalization dataset reaching a mAP of 0.2974 and deviation from the detection dataset of 0.0859.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Schlagwörter Class Imbalance ; Focal Loss ; Retina-net ; Terms— Endoscopic Artefact Detection
ISSN (print) / ISBN 1613-0073
Zeitschrift CEUR Workshop Proceedings
Quellenangaben Band: 2366 Heft: , Seiten: , Artikelnummer: , Supplement: ,
Verlag RWTH
Verlagsort Aachen
Nichtpatentliteratur Publikationen
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)