binary classification model of supervised cnn deep learning with multiple hidden layers Search Results


multiple atlas mirada  (Mirada Medical Limited)
90
Mirada Medical Limited multiple atlas mirada
Auto‐contouring methods reported for delineation of cardiac substructures in radiation therapy
Multiple Atlas Mirada, supplied by Mirada Medical Limited, 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/multiple atlas mirada/product/Mirada Medical Limited
Average 90 stars, based on 1 article reviews
multiple atlas mirada - by Bioz Stars, 2026-04
90/100 stars
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multi-resolution cnn architecture  (Panoptes Pharma GmbH)
90
Panoptes Pharma GmbH multi-resolution cnn architecture
Workflow and Panoptes architecture (A) A total of 456 patients in the cohorts from CPTAC and TCGA with feature annotations. (B) Overall workflow. I, H&E slide images of endometrial cancers were downloaded from databases; II, slides were separated at the per-patient level into a training, validation, and test set; III, slides were cut into 299 × 299-pixel tiles excluding background, and contaminants and qualified tiles were packaged into TFrecord files for each set; IV, training and validation sets were used to train the convolutional neural networks, and the testing set was used to evaluate trained models; V, activation maps of test set tiles were extracted and dimensionally reduced by tSNE to visualize features, while the per-tile predictions were aggregated back into intact slides; VI, an independent test set with samples from NYU hospitals was used to test the generalizability of selected best-performing models. (C) Slides were cut into paired tile sets at 2.5×, 5×, and 10× equivalent resolution of the same region to prepare for Panoptes. (D) Panoptes architecture with optional 1 × 1 convolutional layer and clinical features branch.
Multi Resolution Cnn Architecture, supplied by Panoptes Pharma GmbH, 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/multi-resolution cnn architecture/product/Panoptes Pharma GmbH
Average 90 stars, based on 1 article reviews
multi-resolution cnn architecture - by Bioz Stars, 2026-04
90/100 stars
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cnn_senet  (Respiratory Motion)
90
Respiratory Motion cnn_senet
Workflow and Panoptes architecture (A) A total of 456 patients in the cohorts from CPTAC and TCGA with feature annotations. (B) Overall workflow. I, H&E slide images of endometrial cancers were downloaded from databases; II, slides were separated at the per-patient level into a training, validation, and test set; III, slides were cut into 299 × 299-pixel tiles excluding background, and contaminants and qualified tiles were packaged into TFrecord files for each set; IV, training and validation sets were used to train the convolutional neural networks, and the testing set was used to evaluate trained models; V, activation maps of test set tiles were extracted and dimensionally reduced by tSNE to visualize features, while the per-tile predictions were aggregated back into intact slides; VI, an independent test set with samples from NYU hospitals was used to test the generalizability of selected best-performing models. (C) Slides were cut into paired tile sets at 2.5×, 5×, and 10× equivalent resolution of the same region to prepare for Panoptes. (D) Panoptes architecture with optional 1 × 1 convolutional layer and clinical features branch.
Cnn Senet, supplied by Respiratory Motion, 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/cnn_senet/product/Respiratory Motion
Average 90 stars, based on 1 article reviews
cnn_senet - by Bioz Stars, 2026-04
90/100 stars
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resnet18-softmax  (SoftMax Inc)
90
SoftMax Inc resnet18-softmax
Workflow and Panoptes architecture (A) A total of 456 patients in the cohorts from CPTAC and TCGA with feature annotations. (B) Overall workflow. I, H&E slide images of endometrial cancers were downloaded from databases; II, slides were separated at the per-patient level into a training, validation, and test set; III, slides were cut into 299 × 299-pixel tiles excluding background, and contaminants and qualified tiles were packaged into TFrecord files for each set; IV, training and validation sets were used to train the convolutional neural networks, and the testing set was used to evaluate trained models; V, activation maps of test set tiles were extracted and dimensionally reduced by tSNE to visualize features, while the per-tile predictions were aggregated back into intact slides; VI, an independent test set with samples from NYU hospitals was used to test the generalizability of selected best-performing models. (C) Slides were cut into paired tile sets at 2.5×, 5×, and 10× equivalent resolution of the same region to prepare for Panoptes. (D) Panoptes architecture with optional 1 × 1 convolutional layer and clinical features branch.
Resnet18 Softmax, supplied by SoftMax Inc, 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/resnet18-softmax/product/SoftMax Inc
Average 90 stars, based on 1 article reviews
resnet18-softmax - by Bioz Stars, 2026-04
90/100 stars
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mask r-cnn model  (Perio Products Ltd)
90
Perio Products Ltd mask r-cnn model
Workflow and Panoptes architecture (A) A total of 456 patients in the cohorts from CPTAC and TCGA with feature annotations. (B) Overall workflow. I, H&E slide images of endometrial cancers were downloaded from databases; II, slides were separated at the per-patient level into a training, validation, and test set; III, slides were cut into 299 × 299-pixel tiles excluding background, and contaminants and qualified tiles were packaged into TFrecord files for each set; IV, training and validation sets were used to train the convolutional neural networks, and the testing set was used to evaluate trained models; V, activation maps of test set tiles were extracted and dimensionally reduced by tSNE to visualize features, while the per-tile predictions were aggregated back into intact slides; VI, an independent test set with samples from NYU hospitals was used to test the generalizability of selected best-performing models. (C) Slides were cut into paired tile sets at 2.5×, 5×, and 10× equivalent resolution of the same region to prepare for Panoptes. (D) Panoptes architecture with optional 1 × 1 convolutional layer and clinical features branch.
Mask R Cnn Model, supplied by Perio Products 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/mask r-cnn model/product/Perio Products Ltd
Average 90 stars, based on 1 article reviews
mask r-cnn model - by Bioz Stars, 2026-04
90/100 stars
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panoptes-based multi-resolution cnn models  (Panoptes Pharma GmbH)
90
Panoptes Pharma GmbH panoptes-based multi-resolution cnn models
Workflow and Panoptes architecture (A) A total of 456 patients in the cohorts from CPTAC and TCGA with feature annotations. (B) Overall workflow. I, H&E slide images of endometrial cancers were downloaded from databases; II, slides were separated at the per-patient level into a training, validation, and test set; III, slides were cut into 299 × 299-pixel tiles excluding background, and contaminants and qualified tiles were packaged into TFrecord files for each set; IV, training and validation sets were used to train the convolutional neural networks, and the testing set was used to evaluate trained models; V, activation maps of test set tiles were extracted and dimensionally reduced by tSNE to visualize features, while the per-tile predictions were aggregated back into intact slides; VI, an independent test set with samples from NYU hospitals was used to test the generalizability of selected best-performing models. (C) Slides were cut into paired tile sets at 2.5×, 5×, and 10× equivalent resolution of the same region to prepare for Panoptes. (D) Panoptes architecture with optional 1 × 1 convolutional layer and clinical features branch.
Panoptes Based Multi Resolution Cnn Models, supplied by Panoptes Pharma GmbH, 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/panoptes-based multi-resolution cnn models/product/Panoptes Pharma GmbH
Average 90 stars, based on 1 article reviews
panoptes-based multi-resolution cnn models - by Bioz Stars, 2026-04
90/100 stars
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multi-feature guided convolutional neural network (cnn)  (MathWorks Inc)
90
MathWorks Inc multi-feature guided convolutional neural network (cnn)
<t>Convolutional</t> <t>neural</t> <t>network</t> .
Multi Feature Guided Convolutional Neural Network (Cnn), supplied by MathWorks Inc, 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/multi-feature guided convolutional neural network (cnn)/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
multi-feature guided convolutional neural network (cnn) - by Bioz Stars, 2026-04
90/100 stars
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multi-layer cnn  (MathWorks Inc)
90
MathWorks Inc multi-layer cnn
<t>Convolutional</t> <t>neural</t> <t>network</t> .
Multi Layer Cnn, supplied by MathWorks Inc, 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/multi-layer cnn/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
multi-layer cnn - by Bioz Stars, 2026-04
90/100 stars
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paired multi-scale 3d cnn  (Optics and Photonics)
90
Optics and Photonics paired multi-scale 3d cnn
<t>Convolutional</t> <t>neural</t> <t>network</t> .
Paired Multi Scale 3d Cnn, supplied by Optics and Photonics, 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/paired multi-scale 3d cnn/product/Optics and Photonics
Average 90 stars, based on 1 article reviews
paired multi-scale 3d cnn - by Bioz Stars, 2026-04
90/100 stars
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deep multiple instance cnn  (IEEE Access)
90
IEEE Access deep multiple instance cnn
<t>Convolutional</t> <t>neural</t> <t>network</t> .
Deep Multiple Instance Cnn, supplied by IEEE Access, 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 multiple instance cnn/product/IEEE Access
Average 90 stars, based on 1 article reviews
deep multiple instance cnn - by Bioz Stars, 2026-04
90/100 stars
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nas-optimized multi-precision cnn models vgg16  (Xilinx Inc)
90
Xilinx Inc nas-optimized multi-precision cnn models vgg16
<t>Convolutional</t> <t>neural</t> <t>network</t> .
Nas Optimized Multi Precision Cnn Models Vgg16, supplied by Xilinx Inc, 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/nas-optimized multi-precision cnn models vgg16/product/Xilinx Inc
Average 90 stars, based on 1 article reviews
nas-optimized multi-precision cnn models vgg16 - by Bioz Stars, 2026-04
90/100 stars
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multi-column cnn  (SoftMax Inc)
90
SoftMax Inc multi-column cnn
<t>Convolutional</t> <t>neural</t> <t>network</t> .
Multi Column Cnn, supplied by SoftMax Inc, 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/multi-column cnn/product/SoftMax Inc
Average 90 stars, based on 1 article reviews
multi-column cnn - by Bioz Stars, 2026-04
90/100 stars
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Image Search Results


Auto‐contouring methods reported for delineation of cardiac substructures in radiation therapy

Journal: Journal of Medical Imaging and Radiation Oncology

Article Title: Cardiac substructure delineation in radiation therapy – A state‐of‐the‐art review

doi: 10.1111/1754-9485.13668

Figure Lengend Snippet: Auto‐contouring methods reported for delineation of cardiac substructures in radiation therapy

Article Snippet: 10 , Choi et al . (2020) , Breast , RT sim CT CE* (No breathing info) , Multiple Atlas Mirada & MIM DL 3D CNN (DenseNet) Commercial and in‐house , 6 LA, LV, RA, RV LAD, RCA No guidelines specified , 35 (atlas) 48 (DL)^ , 14 , DSC HD95 , – , – , *Sensitivity analysis conducted using NCE CT, but this did not include cardiac substructures ^DL method used same 35 cases as the atlas method plus 13 additional validation scans Also evaluated auto‐contouring for breast CTVs and other OARs using the same methods DL superior to atlas for cardiac substructure delineation.

Techniques: Imaging, Derivative Assay, Software, Diagnostic Assay, Biomarker Discovery, Modification, Blocking Assay, Construct

Workflow and Panoptes architecture (A) A total of 456 patients in the cohorts from CPTAC and TCGA with feature annotations. (B) Overall workflow. I, H&E slide images of endometrial cancers were downloaded from databases; II, slides were separated at the per-patient level into a training, validation, and test set; III, slides were cut into 299 × 299-pixel tiles excluding background, and contaminants and qualified tiles were packaged into TFrecord files for each set; IV, training and validation sets were used to train the convolutional neural networks, and the testing set was used to evaluate trained models; V, activation maps of test set tiles were extracted and dimensionally reduced by tSNE to visualize features, while the per-tile predictions were aggregated back into intact slides; VI, an independent test set with samples from NYU hospitals was used to test the generalizability of selected best-performing models. (C) Slides were cut into paired tile sets at 2.5×, 5×, and 10× equivalent resolution of the same region to prepare for Panoptes. (D) Panoptes architecture with optional 1 × 1 convolutional layer and clinical features branch.

Journal: Cell Reports Medicine

Article Title: Predicting endometrial cancer subtypes and molecular features from histopathology images using multi-resolution deep learning models

doi: 10.1016/j.xcrm.2021.100400

Figure Lengend Snippet: Workflow and Panoptes architecture (A) A total of 456 patients in the cohorts from CPTAC and TCGA with feature annotations. (B) Overall workflow. I, H&E slide images of endometrial cancers were downloaded from databases; II, slides were separated at the per-patient level into a training, validation, and test set; III, slides were cut into 299 × 299-pixel tiles excluding background, and contaminants and qualified tiles were packaged into TFrecord files for each set; IV, training and validation sets were used to train the convolutional neural networks, and the testing set was used to evaluate trained models; V, activation maps of test set tiles were extracted and dimensionally reduced by tSNE to visualize features, while the per-tile predictions were aggregated back into intact slides; VI, an independent test set with samples from NYU hospitals was used to test the generalizability of selected best-performing models. (C) Slides were cut into paired tile sets at 2.5×, 5×, and 10× equivalent resolution of the same region to prepare for Panoptes. (D) Panoptes architecture with optional 1 × 1 convolutional layer and clinical features branch.

Article Snippet: Overall, we demonstrated that our multi-resolution CNN architecture, Panoptes, can be developed into a practical tool to assist pathologists’ classifying endometrial cancer histological subtypes and, more important, to provide additional information about patients’ molecular subtypes and mutation status in a much more rapid fashion and without the need for sequencing.

Techniques: Biomarker Discovery, Activation Assay

Convolutional neural network .

Journal: Diagnostics

Article Title: Bone Age Assessment Empowered with Deep Learning: A Survey, Open Research Challenges and Future Directions

doi: 10.3390/diagnostics10100781

Figure Lengend Snippet: Convolutional neural network .

Article Snippet: Puyang Wang et al. [ ] , Custom developed US images based (519 Samples) , CNN , Multi-Feature Guided Convolutional Neural Network (CNN) , MATLAB , Recall = 0.97 Precision = 0.965 F-score = 0.968.

Techniques:

Structure of filter-layer-guided convolutional neural network (CNN) .

Journal: Diagnostics

Article Title: Bone Age Assessment Empowered with Deep Learning: A Survey, Open Research Challenges and Future Directions

doi: 10.3390/diagnostics10100781

Figure Lengend Snippet: Structure of filter-layer-guided convolutional neural network (CNN) .

Article Snippet: Puyang Wang et al. [ ] , Custom developed US images based (519 Samples) , CNN , Multi-Feature Guided Convolutional Neural Network (CNN) , MATLAB , Recall = 0.97 Precision = 0.965 F-score = 0.968.

Techniques:

Overview of recent studies for segmentation using deep learning.

Journal: Diagnostics

Article Title: Bone Age Assessment Empowered with Deep Learning: A Survey, Open Research Challenges and Future Directions

doi: 10.3390/diagnostics10100781

Figure Lengend Snippet: Overview of recent studies for segmentation using deep learning.

Article Snippet: Puyang Wang et al. [ ] , Custom developed US images based (519 Samples) , CNN , Multi-Feature Guided Convolutional Neural Network (CNN) , MATLAB , Recall = 0.97 Precision = 0.965 F-score = 0.968.

Techniques: Standard Deviation

Overview of recent deep-learning development for prediction.

Journal: Diagnostics

Article Title: Bone Age Assessment Empowered with Deep Learning: A Survey, Open Research Challenges and Future Directions

doi: 10.3390/diagnostics10100781

Figure Lengend Snippet: Overview of recent deep-learning development for prediction.

Article Snippet: Puyang Wang et al. [ ] , Custom developed US images based (519 Samples) , CNN , Multi-Feature Guided Convolutional Neural Network (CNN) , MATLAB , Recall = 0.97 Precision = 0.965 F-score = 0.968.

Techniques: Plasmid Preparation, Software

Overview of recent deep-learning development for classification.

Journal: Diagnostics

Article Title: Bone Age Assessment Empowered with Deep Learning: A Survey, Open Research Challenges and Future Directions

doi: 10.3390/diagnostics10100781

Figure Lengend Snippet: Overview of recent deep-learning development for classification.

Article Snippet: Puyang Wang et al. [ ] , Custom developed US images based (519 Samples) , CNN , Multi-Feature Guided Convolutional Neural Network (CNN) , MATLAB , Recall = 0.97 Precision = 0.965 F-score = 0.968.

Techniques: Blocking Assay