Transfer learning for endoscopy disease detection and segmentation with mask-RCNN benchmark architecture
Rezvy, Shahadate ORCID: https://orcid.org/0000-0002-2684-7117, Zebin, Tahmina
ORCID: https://orcid.org/0000-0003-0437-0570, Pang, Wei, Taylor, Stephen and Gao, Xiaohong W.
ORCID: https://orcid.org/0000-0002-8103-6624
(2020)
Transfer learning for endoscopy disease detection and segmentation with mask-RCNN benchmark architecture.
Proceedings of the 2nd International Workshop and Challenge on Computer Vision in Endoscopy.
In: EndoCV2020, 03 Apr 2020, Iowa City, United States.
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ISSN 1613-0073
[Conference or Workshop Item]
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Abstract
We proposed and implemented a disease detection and semantic segmentation pipeline using a modified mask-RCNN infrastructure model on the EDD2020 dataset1. On the images provided for the phase-I test dataset, for ’BE’, we achieved an average precision of 51.14%, for ’HGD’ and ’polyp’ it is 50%. However, the detection score for ’suspicious’ and ’cancer’ were low. For phase-I, we achieved a dice coefficient of 0.4562 and an F2 score of 0.4508. We noticed the missed and mis-classification was due to the imbalance between classes. Hence, we applied a selective and balanced augmentation stage in our architecture to provide more accurate detection and segmentation. We observed an increase in detection score to 0.29 on phase-II images after balancing the dataset from our phase-I detection score of 0.24. We achieved an improved semantic segmentation score of 0.62 from our phase-I score of 0.52.
Item Type: | Conference or Workshop Item (Paper) |
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Additional Information: | EndoCV2020 was held in conjunction with the 17th IEEE International Symposium on Biomedical Imaging (ISBI2020). |
Keywords (uncontrolled): | deep learning, computer vision, endoscopy, gastrointestinal |
Research Areas: | A. > School of Science and Technology > Computer Science |
Item ID: | 31104 |
Notes on copyright: | © 2020 for the individual papers by the papers' authors.
© 2020 for the volume as a collection by its editors. This volume and its papers are published under the Creative Commons License Attribution 4.0 International (CC BY 4.0). |
Useful Links: | |
Depositing User: | Xiaohong Gao |
Date Deposited: | 05 Oct 2020 08:02 |
Last Modified: | 05 Oct 2020 08:02 |
URI: | https://eprints.mdx.ac.uk/id/eprint/31104 |
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