cnn-based system Search Results


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IEEE Access auto-zooming cnn-based framework for real-time pedestrian detection in outdoor surveillance videos
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IEEE Access 3d cnn based automatic diagnosis of attention deficit hyperactivity disorder
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Optellum Ltd cnn-based pfn classifier
Performance <t>of</t> <t>PFN-CNN</t> for the classification of typical PFNs
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IEEE Access automated cnn-based tooth segmentation
Performance <t>of</t> <t>PFN-CNN</t> for the classification of typical PFNs
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Respiratory Motion motion correction for free-breathing 3d abdominal mri using cnn based image registration
Performance <t>of</t> <t>PFN-CNN</t> for the classification of typical PFNs
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Mendeley Ltd cnn-based segmentation
Performance <t>of</t> <t>PFN-CNN</t> for the classification of typical PFNs
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IEEE Access prometeo: a cnn-based computer-aided diagnosis system
Performance <t>of</t> <t>PFN-CNN</t> for the classification of typical PFNs
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Deepmind Technologies Ltd cnn-based autocontouring tool
Performance <t>of</t> <t>PFN-CNN</t> for the classification of typical PFNs
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Image Search Results


Performance of PFN-CNN for the classification of typical PFNs

Journal: European Radiology

Article Title: Evaluation of a novel deep learning–based classifier for perifissural nodules

doi: 10.1007/s00330-020-07509-x

Figure Lengend Snippet: Performance of PFN-CNN for the classification of typical PFNs

Article Snippet: The CNN-based PFN classifier was developed by Optellum Ltd. and was initialized from a lung cancer prediction model trained on around 16,000 NLST nodule images, for the task of distinguishing malignant from benign nodules based on analyzing a cuboidal volume of CT data centered on each nodule.

Techniques:

Examples from the test dataset along with scores generated by the PFN-CNN

Journal: European Radiology

Article Title: Evaluation of a novel deep learning–based classifier for perifissural nodules

doi: 10.1007/s00330-020-07509-x

Figure Lengend Snippet: Examples from the test dataset along with scores generated by the PFN-CNN

Article Snippet: The CNN-based PFN classifier was developed by Optellum Ltd. and was initialized from a lung cancer prediction model trained on around 16,000 NLST nodule images, for the task of distinguishing malignant from benign nodules based on analyzing a cuboidal volume of CT data centered on each nodule.

Techniques: Generated

Examples from the test dataset along with scores generated by the PFN-CNN

Journal: European Radiology

Article Title: Evaluation of a novel deep learning–based classifier for perifissural nodules

doi: 10.1007/s00330-020-07509-x

Figure Lengend Snippet: Examples from the test dataset along with scores generated by the PFN-CNN

Article Snippet: The CNN-based PFN classifier was developed by Optellum Ltd. and was initialized from a lung cancer prediction model trained on around 16,000 NLST nodule images, for the task of distinguishing malignant from benign nodules based on analyzing a cuboidal volume of CT data centered on each nodule.

Techniques: Generated