framework for no-code development of deep learning and the neural network console (Sony)
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framework for no-code development of deep learning and the neural network console
Framework For No Code Development Of Deep Learning And The Neural Network Console, supplied by Sony, 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/product/neural+network+development+code/deep+learning+platform+neural+network+console/pmc10216583-102-5-17
Average 90 stars, based on 1 article reviews
Framework For No Code Development Of Deep Learning And The Neural Network Console, supplied by Sony, 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/product/neural+network+development+code/deep+learning+platform+neural+network+console/pmc10216583-102-5-17
Average 90 stars, based on 1 article reviews
framework for no-code development of deep learning and the neural network console - by Bioz Stars,
2026-11
90/100 stars
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other:Article Title: Error detection model developed using a multi-task convolutional neural network in patient-specific quality assurance for volumetric-modulated arc therapy. Article Snippet: through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record.. Please cite this article as doi: 10.1002/MP.15031 This article is protected by copyright.. All rights reserved Error detection model developed using a multi-task convolutional neural network in patient-specific quality assurance for volumetric-modulated arc therapy Article Title: Improvement of deep learning prediction model in patient-specific QA for VMAT with MLC leaf position map and patient's dose distribution. Article Snippet: In addition, a random drop layer was employed to prevent overfitting; the drop rate was set at 0.3.23 An open platform (Neural Network Console, Sony, Japan) was used to build and train the model, with the number of epochs set to 200, batch size to 4, optimization algorithm to Adam, and learning rate to 0.001. Article Title: Right ventricular strain and volume analyses through deep learning-based fully automatic segmentation based on radial long-axis reconstruction of short-axis cine magnetic resonance images. Article Snippet: Objective We propose a deep learning-based fully automatic right ventricle (RV) segmentation technique that targets radially reconstructed long-axis (RLA) images of the center of the RV region in routine short axis (SA) cardiovascular magnetic resonance (CMR) images.. Accordingly, the purpose of this study is to compare the accuracy of deep learning-based fully automatic segmentation of RLA images with the accuracy of conventional deep learning-based segmentation in SA orientation in terms of the measurements of RV strain parameters.. Materials and methods We compared the accuracies of the above-mentioned methods in RV segmentations and in measuring RV strain parameters by Dice similarity coefficients (DSCs) and correlation coefficients. Article Title: Automated Estimation of Mammary Gland Content Ratio Using Regression Deep Convolutional Neural Network and the Effectiveness in Clinical Practice as Explainable Artificial Intelligence Article Snippet: We used a framework for no-code development of deep learning and the neural network console [ ] (Sony Corporation, Tokyo) and varied the architecture of the regression DCNN by modifying the convolution layers and parameters of the kernel. Article Title: Tracheal Sound Analysis Using a Deep Neural Network to Detect Sleep Apnea Article Snippet: We tried to discriminate these images using several types of DNNs, including convolutional (two to four convolutional layers with one to two fully connected layers) and recurrent DNNs, on a PC-based deep learning tool (Neural Network Console; Sony Network Communications Inc., Tokyo, Japan). Article Title: Assessment of Bi-Ventricular and Bi-Atrial Areas Using Four-Chamber Cine Cardiovascular Magnetic Resonance Imaging: Fully Automated Segmentation with a U-Net Convolutional Neural Network Article Snippet: To develop an established CNN, we used a deep learning platform called Neural Network Console (Sony Network Communications, Tokyo, Japan) and a specialized graphic processing unit (GeForce GTX 1080; Nvidia, Santa Clara, CA, USA) [ ]. Article Title: Deep learning for preliminary profiling of panoramic images. Article Snippet: Objective This study explored the feasibility of using deep learning for profiling of panoramic radiographs.. Study design Panoramic radiographs of 1000 patients were used.. Patients were categorized using seven dental or physical characteristics: age, gender, mixed or permanent dentition, number of presenting teeth, impacted wisdom tooth status, implant status, and prosthetic treatment status. Article Title: Image quality improvement of single-shot turbo spin-echo magnetic resonance imaging of female pelvis using a convolutional neural network Article Snippet: All the CNN algorithms were implemented with the deep |