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. . 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)
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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