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A deep convolutional neural network for bleeding detection in Wireless Capsule Endoscopy images

https://doi.org/10.1109/embc.2016.7590783

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inferred← citeshttps://doi.org/10.1007/978-3-030-32239-7_33openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-3-030-41862-5_90openalex-snapshot

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AI and Endoscopy: Future Perspectives

inferred← citeshttps://doi.org/10.1007/978-3-030-69951-2_11openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-3-319-65981-7_12openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.bspc.2019.101565openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.bspc.2021.102929openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.compbiomed.2019.103478openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.compbiomed.2020.104094openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.compbiomed.2021.104789openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.future.2023.01.011openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.gie.2018.10.027openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.gie.2020.04.039openalex-snapshot

Deep learning for wireless capsule endoscopy: a systematic review and meta-analysis

inferred← citeshttps://doi.org/10.1016/j.gie.2020.04.044openalex-snapshot

Proceedings from the First Global Artificial Intelligence in Gastroenterology and Endoscopy Summit

inferred← citeshttps://doi.org/10.1016/j.gie.2020.04.080openalex-snapshot

Automatic detection of various abnormalities in capsule endoscopy videos by a deep learning-based system: a multicenter study

inferred← citeshttps://doi.org/10.1016/j.gie.2020.05.066openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.media.2022.102587openalex-snapshot

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Deep neural networks in the cloud: Review, applications, challenges and research directions

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inferred← citeshttps://doi.org/10.1016/j.vgie.2017.08.012openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.vgie.2020.08.013openalex-snapshot

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inferred← citeshttps://doi.org/10.1038/s41598-020-60969-5openalex-snapshot

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inferred← citeshttps://doi.org/10.1038/s41598-020-74668-8openalex-snapshot

Automatic evaluation of degree of cleanliness in capsule endoscopy based on a novel CNN architecture

inferred← citeshttps://doi.org/10.1038/s41598-021-96748-zopenalex-snapshot

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inferred← citeshttps://doi.org/10.1038/s41598-023-40179-5openalex-snapshot

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inferred← citeshttps://doi.org/10.1049/iet-ipr.2018.6232openalex-snapshot

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inferred← citeshttps://doi.org/10.1049/ipr2.12495openalex-snapshot

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inferred← citeshttps://doi.org/10.1049/ipr2.12704openalex-snapshot

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inferred← citeshttps://doi.org/10.1051/e3sconf/202129701060openalex-snapshot

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inferred← citeshttps://doi.org/10.1053/j.gastro.2019.08.058openalex-snapshot

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inferred← citeshttps://doi.org/10.1063/5.0172064openalex-snapshot

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inferred← citeshttps://doi.org/10.1080/10255842.2022.2125806openalex-snapshot

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inferred← citeshttps://doi.org/10.1080/13645706.2023.2250445openalex-snapshot

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inferred← citeshttps://doi.org/10.1080/17474124.2020.1779058openalex-snapshot

Utilizing artificial intelligence in endoscopy: a clinician’s guide

inferred← citeshttps://doi.org/10.1080/17474124.2026.2646299openalex-snapshot

A critical review of technical progress, clinical heterogeneity, and implementation challenges of artificial intelligence in digestive endoscopy

inferred← citeshttps://doi.org/10.1088/1361-6560/aad51copenalex-snapshot

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inferred← citeshttps://doi.org/10.1088/1757-899x/1022/1/012020openalex-snapshot

Biomedical image classification using deep convolutional neural networks – overview

inferred← citeshttps://doi.org/10.1097/cm9.0000000000000623openalex-snapshot

Artificial intelligence in gastrointestinal endoscopy: general overview

inferred← citeshttps://doi.org/10.1097/mcg.0000000000001423openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/access.2019.2901568openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/access.2019.2944676openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/access.2020.2991115openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/access.2020.3019888openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/access.2022.3201515openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/access.2023.3290997openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/access.2023.3319068openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/cds52072.2021.00016openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/embc.2017.8037242openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/embc.2017.8037526openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/isbi.2017.7950496openalex-snapshot

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inferred← citeshttps://doi.org/10.1111/jgh.14941openalex-snapshot

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inferred← citeshttps://doi.org/10.17762/ijritcc.v11i9.10083openalex-snapshot

Intelligent Hemorrhage Identification in Wireless Capsule Endoscopy Pictures Using AI Techniques.

inferred← citeshttps://doi.org/10.20517/2574-1225.2023.102openalex-snapshot

Artificial intelligence in capsule endoscopy: development status and future expectations

inferred← citeshttps://doi.org/10.21037/atm-21-3001openalex-snapshot

Application of artificial intelligence in gastrointestinal disease: a narrative review

inferred← citeshttps://doi.org/10.21037/atm-22-1914openalex-snapshot

Automatic bleeding detection in laparoscopic surgery based on a faster region-based convolutional neural network

inferred← citeshttps://doi.org/10.21037/dmr-20-162openalex-snapshot

A narrative review of recent progress and current perspectives on video capsule endoscopy

inferred← citeshttps://doi.org/10.21037/tgh.2020.02.01openalex-snapshot

Video capsule endoscopy: pushing the boundaries with software technology

inferred← citeshttps://doi.org/10.2174/0929867328666210405114938openalex-snapshot

Recent Applications of Artificial Intelligence in the Detection of Gastrointestinal, Hepatic and Pancreatic Diseases

inferred← citeshttps://doi.org/10.2174/1573405616666200124140915openalex-snapshot

Colored Video Analysis in Wireless Capsule Endoscopy: A Survey of State-of-the-Art

inferred← citeshttps://doi.org/10.2196/10788openalex-snapshot

Detection of Bleeding Events in Electronic Health Record Notes Using Convolutional Neural Network Models Enhanced With Recurrent Neural Network Autoencoders: Deep Learning Approach

inferred← citeshttps://doi.org/10.2196/33267openalex-snapshot

Computer-Aided Diagnosis of Gastrointestinal Ulcer and Hemorrhage Using Wireless Capsule Endoscopy: Systematic Review and Diagnostic Test Accuracy Meta-analysis

inferred← citeshttps://doi.org/10.22161/ijaems.910.2openalex-snapshot

Deep Learning Neural Networks in the Cloud

inferred← citeshttps://doi.org/10.22214/ijraset.2017.11393openalex-snapshot

A Survey on WCE Abnormality Detection Methods

inferred← citeshttps://doi.org/10.2298/csis201030049sopenalex-snapshot

Explainable information retrieval using deep learning for medical images

inferred← citeshttps://doi.org/10.23919/fruct.2019.8711921openalex-snapshot

Method of Endoscopic Images Analysis for Automatic Bleeding Detection and Segmentation

inferred← citeshttps://doi.org/10.32692/ijdi-eret/12.1.2023.2309openalex-snapshot

Machine Learning in Healthcare: A Review

inferred← citeshttps://doi.org/10.3329/gubjse.v5i1.47897openalex-snapshot

Lung Cancer Detection Using Marker Controlled Watershed with SVM

inferred← citeshttps://doi.org/10.3389/fmicb.2022.1048478openalex-snapshot

A capsule network-based method for identifying transcription factors

inferred← citeshttps://doi.org/10.3390/app10113842openalex-snapshot

Automated Detection and Segmentation of Early Gastric Cancer from Endoscopic Images Using Mask R-CNN

inferred← citeshttps://doi.org/10.3390/diagnostics11071183openalex-snapshot

A Current and Newly Proposed Artificial Intelligence Algorithm for Reading Small Bowel Capsule Endoscopy

inferred← citeshttps://doi.org/10.3390/diagnostics11071192openalex-snapshot

Role of Artificial Intelligence in Video Capsule Endoscopy

inferred← citeshttps://doi.org/10.3390/diagnostics11091719openalex-snapshot

A New Dawn for the Use of Artificial Intelligence in Gastroenterology, Hepatology and Pancreatology

inferred← citeshttps://doi.org/10.3390/diagnostics11091722openalex-snapshot

Artificial Intelligence in Capsule Endoscopy: A Practical Guide to Its Past and Future Challenges

inferred← citeshttps://doi.org/10.3390/diagnostics11112139openalex-snapshot

Small-Bowel Capsule Endoscopy—Optimizing Capsule Endoscopy in Clinical Practice

inferred← citeshttps://doi.org/10.3390/diagnostics12081858openalex-snapshot

Small Bowel Detection for Wireless Capsule Endoscopy Using Convolutional Neural Networks with Temporal Filtering

inferred← citeshttps://doi.org/10.3390/diagnostics12102490openalex-snapshot

Deep Learning Multi-Domain Model Provides Accurate Detection and Grading of Mucosal Ulcers in Different Capsule Endoscopy Types

inferred← citeshttps://doi.org/10.3390/diagnostics13040662openalex-snapshot

Artificial Intelligence—The Rising Star in the Field of Gastroenterology and Hepatology

inferred← citeshttps://doi.org/10.3390/diagnostics14030291openalex-snapshot

From Data to Insights: How Is AI Revolutionizing Small-Bowel Endoscopy?

inferred← citeshttps://doi.org/10.3390/jcm11247476openalex-snapshot

Artificial Intelligence in Endoscopic Ultrasound for Pancreatic Cancer: Where Are We Now and What Does the Future Entail?

inferred← citeshttps://doi.org/10.3390/jcm12237355openalex-snapshot

Development of Bleeding Artificial Intelligence Detector (BLAIR) System for Robotic Radical Prostatectomy

inferred← citeshttps://doi.org/10.3390/jcm9020402openalex-snapshot

Diagnosis and Management of Non-Variceal Gastrointestinal Hemorrhage: A Review of Current Guidelines and Future Perspectives

inferred← citeshttps://doi.org/10.3390/jpm13030413openalex-snapshot

A Multi-Task Convolutional Neural Network for Semantic Segmentation and Event Detection in Laparoscopic Surgery

inferred← citeshttps://doi.org/10.3390/min13070982openalex-snapshot

Addressing Geological Challenges in Mineral Resource Estimation: A Comparative Study of Deep Learning and Traditional Techniques

inferred← citeshttps://doi.org/10.3390/s19061265openalex-snapshot

Application of Convolutional Neural Networks for Automated Ulcer Detection in Wireless Capsule Endoscopy Images

inferred← citeshttps://doi.org/10.3390/s23167170openalex-snapshot

Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review

inferred← citeshttps://doi.org/10.35712/aig.v3.i1.13openalex-snapshot

Current advancements in application of artificial intelligence in clinical decision-making by gastroenterologists in gastrointestinal bleeding

inferred← citeshttps://doi.org/10.35882/jeeemi.v4i1.1openalex-snapshot

Artificial Intelligence: A Review of Progress and Prospects in Medicine and Healthcare

inferred← citeshttps://doi.org/10.36401/iddb-23-15openalex-snapshot

Screening and Diagnostic Advances of Artificial Intelligence in Endoscopy

inferred← citeshttps://doi.org/10.37126/aige.v1.i2.33openalex-snapshot

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

inferred← citeshttps://doi.org/10.37126/aige.v5.i1.89138openalex-snapshot

Artificial intelligence: Applications in critical care gastroenterology

inferred← citeshttps://doi.org/10.3748/wjg.v27.i16.1664openalex-snapshot

Artificial intelligence in gastroenterology and hepatology: Status and challenges

inferred← citeshttps://doi.org/10.3748/wjg.v27.i25.3734openalex-snapshot

Artificial intelligence in small intestinal diseases: Application and prospects

inferred← citeshttps://doi.org/10.3748/wjg.v27.i27.4395openalex-snapshot

Role of artificial intelligence in multidisciplinary imaging diagnosis of gastrointestinal diseases

inferred← citeshttps://doi.org/10.3748/wjg.v30.i48.5111openalex-snapshot

Image detection method for multi-category lesions in wireless capsule endoscopy based on deep learning models

inferred← citeshttps://doi.org/10.3748/wjg.v31.i21.107601openalex-snapshot

Classification of pediatric video capsule endoscopy images for small bowel abnormalities using deep learning models

inferred← citeshttps://doi.org/10.3748/wjg.v31.i39.111495openalex-snapshot

Emerging role of artificial intelligence in gastroenterology and hepatology

inferred← citeshttps://doi.org/10.37745/ejcsit.2013/vol12n26574openalex-snapshot

A Survey on Techniques of Wireless Capsule Endoscopy for Image Enhancement and Disease Detection

inferred← citeshttps://doi.org/10.4018/978-1-5225-7131-5.ch010openalex-snapshot

Applications of Machine Learning in Disease Pre-screening

inferred← citeshttps://doi.org/10.4018/978-1-7998-7705-9.ch049openalex-snapshot

Applications of Machine Learning in Disease Pre-screening

inferred← citeshttps://doi.org/10.4103/singaporemedj.smj-2023-187openalex-snapshot

Development and validation of a deep learning system for detection of small bowel pathologies in capsule endoscopy: a pilot study in a Singapore institution

inferred← citeshttps://doi.org/10.4103/sjg.sjg_286_23openalex-snapshot

Artificial intelligence in endoscopy: Overview, applications, and future directions

inferred← citeshttps://doi.org/10.4236/jbise.2021.1412034openalex-snapshot

Detection of Angioectasias and Haemorrhages Incorporated into a Multi-Class Classification Tool for the GI Tract Anomalies by Using Binary CNNs

inferred← citeshttps://doi.org/10.4253/wjge.v10.i10.239openalex-snapshot

Artificial intelligence in gastrointestinal endoscopy: The future is almost here

inferred← citeshttps://doi.org/10.4286/jjmi.89.538openalex-snapshot

Development and clinical usefulness of artificial intelligence on small bowel capsule endoscopy reading

inferred← citeshttps://doi.org/10.48550/arxiv.1704.06825openalex-snapshot

Deep Learning for Medical Image Processing: Overview, Challenges and Future

inferred← citeshttps://doi.org/10.48550/arxiv.1802.07788openalex-snapshot

Segmentation of Bleeding Regions in Wireless Capsule Endoscopy Images an Approach for inside Capsule Video Summarization

inferred← citeshttps://doi.org/10.4995/thesis/10251/183752openalex-snapshot

Clearing the Way in Capsule Endoscopy with Deep Learning and Computer Vision.

inferred← citeshttps://doi.org/10.5009/gnl220507openalex-snapshot

Capsule Endoscopy in Inflammatory Bowel Disease: Panenteric Capsule Endoscopy and Application of Artificial Intelligence

inferred← citeshttps://doi.org/10.5755/j01.itc.53.2.34525openalex-snapshot

Efficient Guided Grad-CAM Tuned Patch Neural Network for Accurate Anomaly Detection in Full Images

inferred← citeshttps://doi.org/10.5759/jscas.28.15openalex-snapshot

Dataset for Surgical AI

inferred← citeshttps://doi.org/10.5946/ce.2018.172openalex-snapshot

Recent Development of Computer Vision Technology to Improve Capsule Endoscopy

inferred← citeshttps://doi.org/10.5946/ce.2018.173openalex-snapshot

Application of Artificial Intelligence in Capsule Endoscopy: Where Are We Now?

inferred← citeshttps://doi.org/10.5946/ce.2023.230openalex-snapshot

As how artificial intelligence is revolutionizing endoscopy

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