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

Adewole, Sodiq; Fernandes, Philip; Jablonski, James; Copland, Andrew; Porter, Michael D.; Syed, Sana; Brown, Donald E.

doi:10.1109/bigdata52589.2021.9671281

Cited by 2 publications

(10 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Branch1 has been used to concentrate the lesion region, branch 2 has been used to extract useful features from the lesion area, and branch 3 has been used to combine global and local features to get the final prediction result. 4 When the available labeled datasets are insufficient to produce a supervised model with improved performance, weakly supervision 1,28 is seeking to gather more labeled data for supervised training and modeling. The labeled data that is accessible is noisy or comes from an unreliable source.…”

Section: Deep Learningmentioning

confidence: 99%

“…Weakly supervise learning used by most researchers for the long video localization in wireless capsule endoscopy. GCNN (Graphical Convolutional Neural Network), 1 unlike CNN, is designed to work with non-Euclidean structured data. The author gathered extensive videos, which will be converted into nodes Count.…”

Section: Deep Learningmentioning

confidence: 99%

“…Then they represented as a graph, the frame in the capsule endoscopy video as the node by connected the relation by the graph's edges. 1 To make this relationship we used a variety of identical measures such as correlation, cosine similarity, and Euclidean distance among the nodes of the graph. 1 In weakly supervise learning with GCNN used pretrained VGG19 1 trained on huge ImageNet datasets after that finetuned to remove inadequate lighting and inferior quality in the video frames.…”

Section: Deep Learningmentioning

confidence: 99%

“…Artificial intelligence has shown remarkable diagnostic abilities in a variety of gastrointestinal medical image sectors 1 and health care sector. 2 Wireless capsule endoscopy is regarded as a valuable tool for diagnosing intestinal illnesses.…”

Section: Introductionmentioning

confidence: 99%

“…The main problem with capsule endoscopy is that images are obtained with inadequate lighting and the capsule camera moves around throughout the digestive tract, resulting in inadequate quality frames. 1 The most popular application of existing machine learning improvements in healthcare is Computer Aided Detection (CAD), 8,9 which is used to detect lesions, such as those present in the gastrointestinal tract.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Review of Deep Learning Performance in Wireless Capsule Endoscopy Images for GI Disease Classification

Habe,

Haataja,

Toivanen

2024

F1000Res

View full text Add to dashboard Cite

Wireless capsule endoscopy is a non-invasive medical imaging modality used for diagnosing and monitoring digestive tract diseases. However, the analysis of images obtained from wireless capsule endoscopy is a challenging task, as the images are of low resolution and often contain a large number of artifacts. In recent years, deep learning has shown great promise in the analysis of medical images, including wireless capsule endoscopy images. This paper provides a review of the current trends and future directions in deep learning for wireless capsule endoscopy. We focus on the recent advances in transfer learning, attention mechanisms, multi-modal learning, automated lesion detection, interpretability and explainability, data augmentation, and edge computing. We also highlight the challenges and limitations of current deep learning methods and discuss the potential future directions for the field. Our review provides insights into the ongoing research and development efforts in the field of deep learning for wireless capsule endoscopy, and can serve as a reference for researchers, clinicians, and engineers working in this area inspection process.

show abstract

Section: Deep Learningmentioning

confidence: 99%

Section: Deep Learningmentioning

confidence: 99%

Section: Deep Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Review of Deep Learning Performance in Wireless Capsule Endoscopy Images for GI Disease Classification

Habe,

Haataja,

Toivanen

2024

F1000Res

View full text Add to dashboard Cite

show abstract

Wireless Capsule Endoscopy Video Summarization using Transfer Learning and Random Forests

Kaur,

Kumar

2023

IJACSA

View full text Add to dashboard Cite

Wireless Capsule Endoscopy (WCE) is a diagnostic technique for identifying gastrointestinal diseases and abnormalities. Gastroenterologists face a considerable challenge when reviewing a lengthy video to identify a disease. The solution to this problem is generating an automated video summarization technique that generates the WCE Video summaries. This paper presents a Video Summarization technique that summarizes the WCE video. The proposed method uses transfer learning and a Random Forest classifier. Using a computationally light and pretrained MobileNetV2 for feature extraction helped deliver results quickly. Managing small datasets and mitigating the overfitting risk was effectively addressed using Random Forest. The Random Forest's hyperparameters are optimized through the use of Bayesian optimization. The approach proposed has achieved an accuracy of 98.75% in disease prediction while significantly reducing the viewing time for the video summary. Furthermore, it has attained an average F-Score of 0.98, demonstrating its efficacy and reliability.

show abstract

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

Cited by 2 publications

References 60 publications

Review of Deep Learning Performance in Wireless Capsule Endoscopy Images for GI Disease Classification

Review of Deep Learning Performance in Wireless Capsule Endoscopy Images for GI Disease Classification

Wireless Capsule Endoscopy Video Summarization using Transfer Learning and Random Forests

Contact Info

Product

Resources

About