3d cnn Search Results


a convolutional neural network (cnn) with a 3d u-net architecture  (Impath Inc)
90
Impath Inc a convolutional neural network (cnn) with a 3d u-net architecture
A Convolutional Neural Network (Cnn) With A 3d U Net Architecture, supplied by Impath Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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3d cnn with a u-net like encoder–decoder architecture  (Jung Diagnostics GmbH)
90
Jung Diagnostics GmbH 3d cnn with a u-net like encoder–decoder architecture
3d Cnn With A U Net Like Encoder–Decoder Architecture, supplied by Jung Diagnostics GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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3d cnn based automatic diagnosis of attention deficit hyperactivity disorder  (IEEE Access)
90
IEEE Access 3d cnn based automatic diagnosis of attention deficit hyperactivity disorder
3d Cnn Based Automatic Diagnosis Of Attention Deficit Hyperactivity Disorder, 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
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mri tumor segmentation with densely connected 3d cnn  (Optics and Photonics)
90
Optics and Photonics mri tumor segmentation with densely connected 3d cnn
Mri Tumor Segmentation With Densely Connected 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/mri tumor segmentation with densely connected 3d cnn/product/Optics and Photonics
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short-range radar-based gesture recognition system using 3d cnn with triplet loss  (IEEE Access)
90
IEEE Access short-range radar-based gesture recognition system using 3d cnn with triplet loss
Short Range Radar Based Gesture Recognition System Using 3d Cnn With Triplet Loss, 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
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motion correction for free-breathing 3d abdominal mri using cnn based image registration  (Respiratory Motion)
90
Respiratory Motion motion correction for free-breathing 3d abdominal mri using cnn based image registration
Motion Correction For Free Breathing 3d Abdominal Mri Using Cnn Based Image Registration, 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
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3d cnn  (ATUM Bio)
90
ATUM Bio 3d cnn
Example workflow 1: Mitochondria segmentation using 2D <t>CNN.</t> ( A ) Conventional workflow. Users first paint the regions of mitochondria of a target EM image using painting software, e.g., VAST lite (1, top) . This mitochondrial segmentation image (ground truth) and the EM image are transferred to Tensorflow/Python for CNN training and inference (2,3; right). Inferred segmentation is then postprocessed (4, left), e.g., using imageJ, proofread and visualized by VAST lite (5, top). Such relays between software packages are necessary. ( B ) UNI-EM dropdown menu. A series of software (a-d) is located for the CNN-based segmentation (1–5). Standard png/tiff file format is used to connect these software packages. ( C ) Workflow in UNI-EM. Extended Dojo supports paint functions (1; top, left) to draw mitochondrial segmentation (top, right). Users can conduct CNN training (2) and inference (3) through a control panel. A labeling function is also implemented for postprocessing (4, each label is denoted by color). These segmented images are proofread by Dojo (5, left), and visualized by the <t>3D</t> annotator (5, right).
3d Cnn, supplied by ATUM Bio, 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/3d cnn/product/ATUM Bio
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3d cnn  (CT International)
90
CT International 3d cnn
The general characteristics and performance of DLAS model from each article included in this review
3d Cnn, supplied by CT International, 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/3d cnn/product/CT International
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3d cnn - by Bioz Stars, 2026-04
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3d-cnn  (Multimed Inc)
90
Multimed Inc 3d-cnn
The general characteristics and performance of DLAS model from each article included in this review
3d Cnn, supplied by Multimed 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/3d-cnn/product/Multimed Inc
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3d-cnn - by Bioz Stars, 2026-04
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plain 3d cnn voxcnn  (Accuray Radiotherapy)
90
Accuray Radiotherapy plain 3d cnn voxcnn
<t>Convolutional</t> <t>neural</t> <t>network</t> <t>(CNN).</t> A CNN contains two basic parts: feature extraction and classification. The feature extraction part consists of successive convolutional and pooling layers. A convolutional layer applies convolutional filters called a kernel to the image for exploring low and high-level structures. These structures are obtained by shifting these kernels, so called convolution, in the image with a set of weights. After multiplying the elements of these kernels with the corresponding receiving field elements, a feature map is obtained. These maps are passed through nonlinear activation function (e.g., a rectified linear unit). The task of pooling layer is to reduce the feature map size and the total number of parameters to be optimized in the network. It works by gathering similar information in the neighborhood of the receptive field and find a representative value (e.g., maximum or average) within this local region. Flatten layer converts matrices from the convolution layers into a one-dimensional array for the next layer. Fully connected layer computes the final outputs using back propagation and gradient descent as for standard artificial neural networks.
Plain 3d Cnn Voxcnn, supplied by Accuray Radiotherapy, 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/plain 3d cnn voxcnn/product/Accuray Radiotherapy
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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> <t>(CNN).</t> A CNN contains two basic parts: feature extraction and classification. The feature extraction part consists of successive convolutional and pooling layers. A convolutional layer applies convolutional filters called a kernel to the image for exploring low and high-level structures. These structures are obtained by shifting these kernels, so called convolution, in the image with a set of weights. After multiplying the elements of these kernels with the corresponding receiving field elements, a feature map is obtained. These maps are passed through nonlinear activation function (e.g., a rectified linear unit). The task of pooling layer is to reduce the feature map size and the total number of parameters to be optimized in the network. It works by gathering similar information in the neighborhood of the receptive field and find a representative value (e.g., maximum or average) within this local region. Flatten layer converts matrices from the convolution layers into a one-dimensional array for the next layer. Fully connected layer computes the final outputs using back propagation and gradient descent as for standard artificial neural networks.
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
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paired multi-scale 3d cnn - by Bioz Stars, 2026-04
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cnn-based, joint 3d reconstruction-and-stitching algorithm  (Photonics Inc)
90
Photonics Inc cnn-based, joint 3d reconstruction-and-stitching algorithm
<t>Convolutional</t> <t>neural</t> <t>network</t> <t>(CNN).</t> A CNN contains two basic parts: feature extraction and classification. The feature extraction part consists of successive convolutional and pooling layers. A convolutional layer applies convolutional filters called a kernel to the image for exploring low and high-level structures. These structures are obtained by shifting these kernels, so called convolution, in the image with a set of weights. After multiplying the elements of these kernels with the corresponding receiving field elements, a feature map is obtained. These maps are passed through nonlinear activation function (e.g., a rectified linear unit). The task of pooling layer is to reduce the feature map size and the total number of parameters to be optimized in the network. It works by gathering similar information in the neighborhood of the receptive field and find a representative value (e.g., maximum or average) within this local region. Flatten layer converts matrices from the convolution layers into a one-dimensional array for the next layer. Fully connected layer computes the final outputs using back propagation and gradient descent as for standard artificial neural networks.
Cnn Based, Joint 3d Reconstruction And Stitching Algorithm, supplied by Photonics 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/cnn-based, joint 3d reconstruction-and-stitching algorithm/product/Photonics Inc
Average 90 stars, based on 1 article reviews
cnn-based, joint 3d reconstruction-and-stitching algorithm - by Bioz Stars, 2026-04
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Image Search Results


Example workflow 1: Mitochondria segmentation using 2D CNN. ( A ) Conventional workflow. Users first paint the regions of mitochondria of a target EM image using painting software, e.g., VAST lite (1, top) . This mitochondrial segmentation image (ground truth) and the EM image are transferred to Tensorflow/Python for CNN training and inference (2,3; right). Inferred segmentation is then postprocessed (4, left), e.g., using imageJ, proofread and visualized by VAST lite (5, top). Such relays between software packages are necessary. ( B ) UNI-EM dropdown menu. A series of software (a-d) is located for the CNN-based segmentation (1–5). Standard png/tiff file format is used to connect these software packages. ( C ) Workflow in UNI-EM. Extended Dojo supports paint functions (1; top, left) to draw mitochondrial segmentation (top, right). Users can conduct CNN training (2) and inference (3) through a control panel. A labeling function is also implemented for postprocessing (4, each label is denoted by color). These segmented images are proofread by Dojo (5, left), and visualized by the 3D annotator (5, right).

Journal: Scientific Reports

Article Title: UNI-EM: An Environment for Deep Neural Network-Based Automated Segmentation of Neuronal Electron Microscopic Images

doi: 10.1038/s41598-019-55431-0

Figure Lengend Snippet: Example workflow 1: Mitochondria segmentation using 2D CNN. ( A ) Conventional workflow. Users first paint the regions of mitochondria of a target EM image using painting software, e.g., VAST lite (1, top) . This mitochondrial segmentation image (ground truth) and the EM image are transferred to Tensorflow/Python for CNN training and inference (2,3; right). Inferred segmentation is then postprocessed (4, left), e.g., using imageJ, proofread and visualized by VAST lite (5, top). Such relays between software packages are necessary. ( B ) UNI-EM dropdown menu. A series of software (a-d) is located for the CNN-based segmentation (1–5). Standard png/tiff file format is used to connect these software packages. ( C ) Workflow in UNI-EM. Extended Dojo supports paint functions (1; top, left) to draw mitochondrial segmentation (top, right). Users can conduct CNN training (2) and inference (3) through a control panel. A labeling function is also implemented for postprocessing (4, each label is denoted by color). These segmented images are proofread by Dojo (5, left), and visualized by the 3D annotator (5, right).

Article Snippet: The segmentation accuracy of the 3D CNN was quantified as Jaccard 0.92, Dice 0.96, and conformity 0.91 (semantic segmentation; ATUM/SEM data), whereas that of our standard 2D CNN was quantified as Jaccard 0.91, Dice 0.95, conformity 0.90 (semantic segmentation).

Techniques: Software, Control, Labeling

Underlying architecture of UNI-EM. UNI-EM has a heterogenous system. Present desktop computers have two types of computational resources: CPU and GPU (top). A GPU is used by Tensorflow for CNN computing (middle), which is not appropriate for shared use. Only the resource monitor Tensorboard can be used by remote users (bottom). Similarly, remote users can use proofreader Dojo and 3D annotator. Only a desktop user (silhouette person) can control all of the UNI-EM functions, including job submission for CNN computing such as training and inference.

Journal: Scientific Reports

Article Title: UNI-EM: An Environment for Deep Neural Network-Based Automated Segmentation of Neuronal Electron Microscopic Images

doi: 10.1038/s41598-019-55431-0

Figure Lengend Snippet: Underlying architecture of UNI-EM. UNI-EM has a heterogenous system. Present desktop computers have two types of computational resources: CPU and GPU (top). A GPU is used by Tensorflow for CNN computing (middle), which is not appropriate for shared use. Only the resource monitor Tensorboard can be used by remote users (bottom). Similarly, remote users can use proofreader Dojo and 3D annotator. Only a desktop user (silhouette person) can control all of the UNI-EM functions, including job submission for CNN computing such as training and inference.

Article Snippet: The segmentation accuracy of the 3D CNN was quantified as Jaccard 0.92, Dice 0.96, and conformity 0.91 (semantic segmentation; ATUM/SEM data), whereas that of our standard 2D CNN was quantified as Jaccard 0.91, Dice 0.95, conformity 0.90 (semantic segmentation).

Techniques: Control

The general characteristics and performance of DLAS model from each article included in this review

Journal: Advances in Radiation Oncology

Article Title: Review of Deep Learning Based Autosegmentation for Clinical Target Volume: Current Status and Future Directions

doi: 10.1016/j.adro.2024.101470

Figure Lengend Snippet: The general characteristics and performance of DLAS model from each article included in this review

Article Snippet: Buelens et al, 2022 , Breast (CT) , International , Yes , 3D CNN vs manual , Yes , 95 , CNN segmentation performance was best for breast CTV and worse for Rotter's space and the internal mammary nodes Guideline consistency improved from 77.14%-90.71% in favor of CNN segmentation , CNN segmentation saved on average 24 min per patient with a median time of 35 min for pure manual segmentation , Not reported.

Techniques: Modification, Selection

Convolutional neural network (CNN). A CNN contains two basic parts: feature extraction and classification. The feature extraction part consists of successive convolutional and pooling layers. A convolutional layer applies convolutional filters called a kernel to the image for exploring low and high-level structures. These structures are obtained by shifting these kernels, so called convolution, in the image with a set of weights. After multiplying the elements of these kernels with the corresponding receiving field elements, a feature map is obtained. These maps are passed through nonlinear activation function (e.g., a rectified linear unit). The task of pooling layer is to reduce the feature map size and the total number of parameters to be optimized in the network. It works by gathering similar information in the neighborhood of the receptive field and find a representative value (e.g., maximum or average) within this local region. Flatten layer converts matrices from the convolution layers into a one-dimensional array for the next layer. Fully connected layer computes the final outputs using back propagation and gradient descent as for standard artificial neural networks.

Journal: Clinical Psychopharmacology and Neuroscience

Article Title: An Overview of Deep Learning Algorithms and Their Applications in Neuropsychiatry

doi: 10.9758/cpn.2021.19.2.206

Figure Lengend Snippet: Convolutional neural network (CNN). A CNN contains two basic parts: feature extraction and classification. The feature extraction part consists of successive convolutional and pooling layers. A convolutional layer applies convolutional filters called a kernel to the image for exploring low and high-level structures. These structures are obtained by shifting these kernels, so called convolution, in the image with a set of weights. After multiplying the elements of these kernels with the corresponding receiving field elements, a feature map is obtained. These maps are passed through nonlinear activation function (e.g., a rectified linear unit). The task of pooling layer is to reduce the feature map size and the total number of parameters to be optimized in the network. It works by gathering similar information in the neighborhood of the receptive field and find a representative value (e.g., maximum or average) within this local region. Flatten layer converts matrices from the convolution layers into a one-dimensional array for the next layer. Fully connected layer computes the final outputs using back propagation and gradient descent as for standard artificial neural networks.

Article Snippet: Korolev et al . 2017 [ ] , Plain 3D CNN (VoxCNN) and ResNet with six VoxRes blocks , 2017 , 3D sMRI , Classification of AD, EMCI, and LMCI , Accuray (VoxCNN) = 79% for NC vs. AD Accuray (VoxCNN) = 63% for NC vs. LMCI Accuray (ResNet) = 54% for AD vs. EMCI Accuray (ResNet) = 80% for NC vs. AD Accuray (ResNet) = 61% for NC vs. LMCI Accuray (ResNet) = 56% for AD vs. EMCI.

Techniques: Extraction, Activation Assay

Flow diagram for study selection (modified from Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement). ANNs, artificial neural networks; CNNs, convolutional neural networks; RNNs, recurrent neural networks; GANs, generative adversarial networks.

Journal: Clinical Psychopharmacology and Neuroscience

Article Title: An Overview of Deep Learning Algorithms and Their Applications in Neuropsychiatry

doi: 10.9758/cpn.2021.19.2.206

Figure Lengend Snippet: Flow diagram for study selection (modified from Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement). ANNs, artificial neural networks; CNNs, convolutional neural networks; RNNs, recurrent neural networks; GANs, generative adversarial networks.

Article Snippet: Korolev et al . 2017 [ ] , Plain 3D CNN (VoxCNN) and ResNet with six VoxRes blocks , 2017 , 3D sMRI , Classification of AD, EMCI, and LMCI , Accuray (VoxCNN) = 79% for NC vs. AD Accuray (VoxCNN) = 63% for NC vs. LMCI Accuray (ResNet) = 54% for AD vs. EMCI Accuray (ResNet) = 80% for NC vs. AD Accuray (ResNet) = 61% for NC vs. LMCI Accuray (ResNet) = 56% for AD vs. EMCI.

Techniques: Selection, Modification

Studies using CNN in neuropsychiatry

Journal: Clinical Psychopharmacology and Neuroscience

Article Title: An Overview of Deep Learning Algorithms and Their Applications in Neuropsychiatry

doi: 10.9758/cpn.2021.19.2.206

Figure Lengend Snippet: Studies using CNN in neuropsychiatry

Article Snippet: Korolev et al . 2017 [ ] , Plain 3D CNN (VoxCNN) and ResNet with six VoxRes blocks , 2017 , 3D sMRI , Classification of AD, EMCI, and LMCI , Accuray (VoxCNN) = 79% for NC vs. AD Accuray (VoxCNN) = 63% for NC vs. LMCI Accuray (ResNet) = 54% for AD vs. EMCI Accuray (ResNet) = 80% for NC vs. AD Accuray (ResNet) = 61% for NC vs. LMCI Accuray (ResNet) = 56% for AD vs. EMCI.

Techniques: Single Photon Emission Computed Tomography