=Paper=
{{Paper
|id=Vol-2595/endoCV2020_Gao_Braden_et_al
|storemode=property
|title=Artefact Detection and Segmentation based on a Deep Learning system
|pdfUrl=https://ceur-ws.org/Vol-2595/endoCV2020_paper_id_s14.pdf
|volume=Vol-2595
|authors=Xiaohong (Sharon) Gao,Barbara Braden
|dblpUrl=https://dblp.org/rec/conf/isbi/GaoB20
}}
==Artefact Detection and Segmentation based on a Deep Learning system==
https://ceur-ws.org/Vol-2595/endoCV2020_paper_id_s14.pdf
ARTEFACT DETECTION AND SEGMENTATION BASED ON A DEEP LEARNING SYSTEM
Xiaohong (Sharon) Gao1 , Barbara Braden2
1
Department of Computer Science, Middlesex University, London, NW4 4BT, UK
2
John Radcliff Hospital, University of Oxford, Oxford, UK
ABSTRACT Model parameters Scored Rank
Epoch = 412,
This paper presents the results of detection and segmentation 0.2020 0.0768 18
Threshold = 0.17
of artefact from endoscopic video frames for EAD2020 com-
Epoch = 226,
petition. In this competition, a deep learning based system 0.2205 0.0843 7
Threshold = 0.13
is applied, which is built upon RetinaNet. Since RetinaNet
employs a one-stage method that lacks facilitating masks of Table 1. Leaderborad scores for the two submissions put in
segmented objects, inspired by the work of real-time instance in the EAD2020 competition.
segmentation, this system accomplishes object segmentation
through two parallel branches to generate a set of prototype mAP at IoU thresholds
masks and to predict per-object mask coefficients respec- Region type
0.50 0.70 0.95 All
tively. Overall, top 7 (out of 32 entries) position was achieved box 77.85 61.64 0.99 47.42
in this competition on the leaderboard. mask 76.91 61.51 0.87 46.17
Table 2. The mAP values for segmentation.
1. METHODS
Figure 1 illustrates the network system applied in this com- [2] Sharib Ali, Felix Zhou, Adam Bailey, Barbara Braden,
petition [1, 2, 3, 4, 5, 6, 7, 8, 9] built upon RetinaNet. It ac- James East, Xin Lu, and Jens Rittscher. A deep learning
complishes object segmentation through two parallel strands framework for quality assessment and restoration in video
(Prototype and Prediction coefficient), which are to generate endoscopy. arXiv preprint arXiv:1904.07073, 2019.
a set of prototype masks and to predict per-object mask co- [3] Sharib Ali, Noha Ghatwary, Barbara Braden, Dominique
efficients respectively. The backbone model of Resnet101 is Lamarque, Adam Bailey, Stefano Realdon, Renato Can-
applied for all three tasks.
2. RESULTS
Table 1 presents the final results submitted from this work
whereas Table 2 gives the mAP for segmentation when 95%
the overall classification accuracy was 63%. Although the
results remain on the top 7, it is felt more enhancement is
needed to further improve this model to improve its robust-
ness.
3. REFERENCES
[1] Sharib Ali, Felix Zhou, Christian Daul, Barbara Braden,
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doscopy artifact detection (ead 2019) challenge dataset.
arXiv preprint arXiv:1905.03209, 2019.
Copyright c 2020 for this paper by its authors. Use permitted under
Creative Commons License Attribution 4.0 International (CC BY 4.0). Fig. 1. The network applied in competition.
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