Graph Convolution Neural Network For Weakly Supervised Abnormality Localization In Long Capsule Endoscopy Videos

• URL: http://arxiv.org/abs/2110.09110v1
• Date: Mon, 18 Oct 2021 09:00:24 GMT
• Title: Graph Convolution Neural Network For Weakly Supervised Abnormality Localization In Long Capsule Endoscopy Videos
• Authors: Sodiq Adewole, Philip Fernandes, James Jablonski, Andrew Copland, Michael Porter, Sana Syed, Donald Brown
• Abstract summary: We propose an end-to-end temporal abnormality localization for long WCE videos using only weak video level labels. Our method achieved an accuracy of 89.9% on the graph classification task and a specificity of 97.5% on the abnormal frames localization task.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Temporal activity localization in long videos is an important problem. The cost of obtaining frame level label for long Wireless Capsule Endoscopy (WCE) videos is prohibitive. In this paper, we propose an end-to-end temporal abnormality localization for long WCE videos using only weak video level labels. Physicians use Capsule Endoscopy (CE) as a non-surgical and non-invasive method to examine the entire digestive tract in order to diagnose diseases or abnormalities. While CE has revolutionized traditional endoscopy procedures, a single CE examination could last up to 8 hours generating as much as 100,000 frames. Physicians must review the entire video, frame-by-frame, in order to identify the frames capturing relevant abnormality. This, sometimes could be as few as just a single frame. Given this very high level of redundancy, analyzing long CE videos can be very tedious, time consuming and also error prone. This paper presents a novel multi-step method for an end-to-end localization of target frames capturing abnormalities of interest in the long video using only weak video labels. First we developed an automatic temporal segmentation using change point detection technique to temporally segment the video into uniform, homogeneous and identifiable segments. Then we employed Graph Convolutional Neural Network (GCNN) to learn a representation of each video segment. Using weak video segment labels, we trained our GCNN model to recognize each video segment as abnormal if it contains at least a single abnormal frame. Finally, leveraging the parameters of the trained GCNN model, we replaced the final layer of the network with a temporal pool layer to localize the relevant abnormal frames within each abnormal video segment. Our method achieved an accuracy of 89.9\% on the graph classification task and a specificity of 97.5\% on the abnormal frames localization task.

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