deep convolutional neural network (dcnn) Search Results


deep convolutional neural network dcnn  (Deepmind Technologies Ltd)
90
Deepmind Technologies Ltd deep convolutional neural network dcnn
Deep Convolutional Neural Network Dcnn, supplied by Deepmind Technologies Ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/deep convolutional neural network dcnn/product/Deepmind Technologies Ltd
Average 90 stars, based on 1 article reviews
deep convolutional neural network dcnn - by Bioz Stars, 2026-03
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deep convolutional neural network (dcnn)  (Optos plc)
90
Optos plc deep convolutional neural network (dcnn)
Deep Convolutional Neural Network (Dcnn), supplied by Optos plc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/deep convolutional neural network (dcnn)/product/Optos plc
Average 90 stars, based on 1 article reviews
deep convolutional neural network (dcnn) - by Bioz Stars, 2026-03
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deep convolutional neural network (dcnn)  (PENTAX Medical Company)
90
PENTAX Medical Company deep convolutional neural network (dcnn)
Development and diagnostic output of the system. (a) The deep <t>convolutional</t> neural network <t>(DCNN)</t> processes video data as a sequence of single video frames and generates predictions based on the visual evidence of a single video frame. The predictions from individual frames are then fused to provide a more stable detection. (b) Different examples of polyp detection with the DCNN during routine colonoscopy. The computer-aided detection (CAD) system generates the diagnostic output on a second screen on which polyps are highlighted by a bounding box. Note that the DCNN is able to detect multiple polyps in a single frame simultaneously (upper right picture).
Deep Convolutional Neural Network (Dcnn), supplied by PENTAX Medical Company, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/deep convolutional neural network (dcnn)/product/PENTAX Medical Company
Average 90 stars, based on 1 article reviews
deep convolutional neural network (dcnn) - by Bioz Stars, 2026-03
90/100 stars
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dcnn (deep convolutional neural network)  (SMAC Corp)
90
SMAC Corp dcnn (deep convolutional neural network)
Development and diagnostic output of the system. (a) The deep <t>convolutional</t> neural network <t>(DCNN)</t> processes video data as a sequence of single video frames and generates predictions based on the visual evidence of a single video frame. The predictions from individual frames are then fused to provide a more stable detection. (b) Different examples of polyp detection with the DCNN during routine colonoscopy. The computer-aided detection (CAD) system generates the diagnostic output on a second screen on which polyps are highlighted by a bounding box. Note that the DCNN is able to detect multiple polyps in a single frame simultaneously (upper right picture).
Dcnn (Deep Convolutional Neural Network), supplied by SMAC Corp, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/dcnn (deep convolutional neural network)/product/SMAC Corp
Average 90 stars, based on 1 article reviews
dcnn (deep convolutional neural network) - by Bioz Stars, 2026-03
90/100 stars
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Image Search Results


Development and diagnostic output of the system. (a) The deep convolutional neural network (DCNN) processes video data as a sequence of single video frames and generates predictions based on the visual evidence of a single video frame. The predictions from individual frames are then fused to provide a more stable detection. (b) Different examples of polyp detection with the DCNN during routine colonoscopy. The computer-aided detection (CAD) system generates the diagnostic output on a second screen on which polyps are highlighted by a bounding box. Note that the DCNN is able to detect multiple polyps in a single frame simultaneously (upper right picture).

Journal: European Journal of Gastroenterology & Hepatology

Article Title: Computer-aided detection of colorectal polyps using a newly generated deep convolutional neural network: from development to first clinical experience

doi: 10.1097/MEG.0000000000002209

Figure Lengend Snippet: Development and diagnostic output of the system. (a) The deep convolutional neural network (DCNN) processes video data as a sequence of single video frames and generates predictions based on the visual evidence of a single video frame. The predictions from individual frames are then fused to provide a more stable detection. (b) Different examples of polyp detection with the DCNN during routine colonoscopy. The computer-aided detection (CAD) system generates the diagnostic output on a second screen on which polyps are highlighted by a bounding box. Note that the DCNN is able to detect multiple polyps in a single frame simultaneously (upper right picture).

Article Snippet: In the current study, we evaluated a novel deep convolutional neural network (DCNN) for automated detection of colorectal polyps that has been developed by a manufacturer of the healthcare industry (Hoya Corporation, Pentax Medical Division, Digital Endoscopy, Friedberg, Germany) in close collaboration with clinical and scientific partners and assessed the performance of the DCNN ex vivo as well as in a first in-human pilot trial.

Techniques: Diagnostic Assay, Sequencing

Patient characteristics and withdrawal times

Journal: European Journal of Gastroenterology & Hepatology

Article Title: Computer-aided detection of colorectal polyps using a newly generated deep convolutional neural network: from development to first clinical experience

doi: 10.1097/MEG.0000000000002209

Figure Lengend Snippet: Patient characteristics and withdrawal times

Article Snippet: In the current study, we evaluated a novel deep convolutional neural network (DCNN) for automated detection of colorectal polyps that has been developed by a manufacturer of the healthcare industry (Hoya Corporation, Pentax Medical Division, Digital Endoscopy, Friedberg, Germany) in close collaboration with clinical and scientific partners and assessed the performance of the DCNN ex vivo as well as in a first in-human pilot trial.

Techniques:

Total number of polyps and adenomas and polyp detection rate and adenoma detection rate after first (without deep convolutional neural network) and second inspection (with deep convolutional neural network)

Journal: European Journal of Gastroenterology & Hepatology

Article Title: Computer-aided detection of colorectal polyps using a newly generated deep convolutional neural network: from development to first clinical experience

doi: 10.1097/MEG.0000000000002209

Figure Lengend Snippet: Total number of polyps and adenomas and polyp detection rate and adenoma detection rate after first (without deep convolutional neural network) and second inspection (with deep convolutional neural network)

Article Snippet: In the current study, we evaluated a novel deep convolutional neural network (DCNN) for automated detection of colorectal polyps that has been developed by a manufacturer of the healthcare industry (Hoya Corporation, Pentax Medical Division, Digital Endoscopy, Friedberg, Germany) in close collaboration with clinical and scientific partners and assessed the performance of the DCNN ex vivo as well as in a first in-human pilot trial.

Techniques:

Characteristics of the polyps detected during first inspection without deep convolutional neural network and those additionally detected during second inspection with deep convolutional neural network

Journal: European Journal of Gastroenterology & Hepatology

Article Title: Computer-aided detection of colorectal polyps using a newly generated deep convolutional neural network: from development to first clinical experience

doi: 10.1097/MEG.0000000000002209

Figure Lengend Snippet: Characteristics of the polyps detected during first inspection without deep convolutional neural network and those additionally detected during second inspection with deep convolutional neural network

Article Snippet: In the current study, we evaluated a novel deep convolutional neural network (DCNN) for automated detection of colorectal polyps that has been developed by a manufacturer of the healthcare industry (Hoya Corporation, Pentax Medical Division, Digital Endoscopy, Friedberg, Germany) in close collaboration with clinical and scientific partners and assessed the performance of the DCNN ex vivo as well as in a first in-human pilot trial.

Techniques: