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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, Braden, Barbara, Pang, Wei, Taylor, Stephen and Gao, Xiaohong W. (2020) Transfer learning for Endoscopy disease detection and segmentation with mask-RCNN benchmark architecture. In: 2nd International Workshop and Challenge on Computer Vision in Endoscopy 2020, 3 Apr 2020, Iowa City, United States.

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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)
Status: Published
School/Department: London Campus
URI: http://ray.yorksj.ac.uk/id/eprint/5896

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