Understanding deep learning in capsule endoscopy: Can artificial intelligence enhance clinical practice?

Atsawarungruangkit, Amporn; Elfanagely, Yousef; Asombang, Akwi W.; Rupawala, Abbas; Rich, Harlan

doi:10.37126/aige.v1.i2.33

Cited by 5 publications

(2 citation statements)

References 40 publications

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“…Typically, the WCE capatures over 50,000 video frames [4], which can be divided as footages ready to be processed using CAD systems [5]. One of the most effective ways to avoid CRC is to remove the precancerous abnormalities.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-based Polyp Detection in Wireless Capsule Endoscopy Images

Saeed

2021

Journal of Engineering Research

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Gastrointestinal (GI) system diseases have increased significantly, where colon and rectum cancer is considered the second cause of death in 2020. Wireless Capsule Endoscopy (WCE) is a revolutionary procedure for detecting Colorectal lesions. It was automatically used to detect the polyps, multiple SB lesions, bleeding, and Ulcer. The acquired video by the WCE can be processed using a Computer-Aided Diagnosis (CAD) system. However, such videos suffer several problems, including burling, high illumination. and distortion. These effects obligate the development of image processing techniques of high accuracy in detection using deep learning-based segmentation. In this paper, a transfer learning-based U-Net was proposed to transfer the knowledge between the medical images in the training phase and the subsequent segmentation using transfer learning to achieve better results and high accuracy results compared to other related studies. The improvement is done by using an algorism written in python codeThe results showed average segmentation accuracy of 98.67%.

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Section: Introductionmentioning

confidence: 99%

Deep Learning-based Polyp Detection in Wireless Capsule Endoscopy Images

Saeed

2021

Journal of Engineering Research

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“…The data from each capsule is a video stream that is 8 to 16 hours long, depending on the capsule model [16,13,2] amounting to frames upwards from 50,000 to be investigated by experts. Moreover, a significant number of these frames contribute from little to no diagnostic information due to either debris and bubbles completely or partially occluding the mucosa or undesirable capsule orientations for long duration during its traversal [22].…”

Section: Introductionmentioning

confidence: 99%

Learning More for Free - A Multi Task Learning Approach for Improved Pathology Classification in Capsule Endoscopy

Vats¹,

Pedersen²,

Mohammed³

et al. 2021

Preprint

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The progress in Computer Aided Diagnosis (CADx) of Wireless Capsule Endoscopy (WCE) is thwarted by the lack of data. The inadequacy in richly representative healthy and abnormal conditions results in isolated analyses of pathologies, that can not handle realistic multipathology scenarios. In this work, we explore how to learn more for free, from limited data through solving a WCE multicentric, multi-pathology classification problem. Learning more implies to learning more than full supervision would allow with the same data. This is done by combining self supervision with full supervision, under multi task learning. Additionally, we draw inspiration from the Human Visual System (HVS) in designing self supervision tasks and investigate if seemingly ineffectual signals within the data itself can be exploited to gain performance, if so, which signals would be better than others. Further, we present our analysis of the high level features as a stepping stone towards more robust multi-pathology CADx in WCE. Code accompanying this work will be made available on github.

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