graphical user interface-based dl tool deep analyzer (Ghelia Inc)
90
Structured Review
Ghelia Inc
graphical user interface-based dl tool deep analyzer
Graphical User Interface Based Dl Tool Deep Analyzer, supplied by Ghelia 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/product/deep+learning+based+algorithms/graphical+user+interface+based+deep+learning+tool+deep+analyzer/pmc07277981-98-40-43
Average 90 stars, based on 1 article reviews
Graphical User Interface Based Dl Tool Deep Analyzer, supplied by Ghelia 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/product/deep+learning+based+algorithms/graphical+user+interface+based+deep+learning+tool+deep+analyzer/pmc07277981-98-40-43
Average 90 stars, based on 1 article reviews
graphical user interface-based dl tool deep analyzer - by Bioz Stars,
2026-09
90/100 stars
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other:Article Title: Deep-learning approach with convolutional neural network for classification of maximum intensity projections of dynamic contrast-enhanced breast magnetic resonance imaging. Article Snippet: Purpose: We aimed to evaluate deep learning approach with convolutional neural networks (CNNs) to discriminate between benign and malignant lesions on maximum intensity projections of dynamic contrast-enhanced breast magnetic resonance imaging (MRI).. Methods: We retrospectively gathered maximum intensity projections of dynamic contrast-enhanced breast MRI of 106 benign (including 22 normal) and 180 malignant cases for training and validation data.. CNN models were constructed to calculate the probability of malignancy using CNN architectures (DenseNet121, DenseNet169, InceptionResNetV2, InceptionV3, NasNetMobile, and Xception) with 500 epochs and analyzed that of 25 benign (including 12 normal) and 47 malignant cases for test data. Article Title: Feasibility of new fat suppression for breast MRI using pix2pix. Article Snippet: Purpose To generate and evaluate fat-saturated T1-weighted (FST1W) image synthesis of breast magnetic resonance imaging (MRI) using pix2pix.. Materials and methods We collected pairs of noncontrast-enhanced T1-weighted an FST1W images of breast MRI for training data (2112 pairs from 15 patients), validation data (428 pairs from three patients), and test data (90 pairs from 30 patients).. From the original images, 90 synthetic images were generated with 50, 100, and 200 epochs using pix2pix. Article Title: Efficacy of ultrasound endoscopy with artificial intelligence for the differential diagnosis of non-gastric gastrointestinal stromal tumors. Article Snippet: The EUS-AI system was created on an AI-specific computer containing a GeForce GTX 1080 graphics processing unit (NVIDIA, California, USA), a core i7-8700 central processing unit (Intel, California, USA), and a deep analyzer graphical user interface-based deep Article Title: Deep learning method with a convolutional neural network for image classification of normal and metastatic axillary lymph nodes on breast ultrasonography. Article Snippet: Purpose To investigate the ability of deep learning (DL) using convolutional neural networks (CNNs) for distinguishing between normal and metastatic axillary lymph nodes on ultrasound images by comparing the diagnostic performance of radiologists.. Materials and methods We retrospectively gathered 300 images of normal and 328 images of axillary lymph nodes with breast cancer metastases for training.. A DL model using the CNN architecture Xception was developed to analyze test data of 50 normal and 50 metastatic lymph nodes. Article Title: Detection and Diagnosis of Breast Cancer Using Artificial Intelligence Based Assessment of Maximum Intensity Projection Dynamic Contrast-Enhanced Magnetic Resonance Images Article Snippet: Our study was performed on a Deep Station system (UEI, Tokyo, Japan) containing the graphics processing unit GeForce GTX 1080 (NVIDIA, Santa Clara, CA, USA), central processing unit Core i7-8700 (Intel, Santa Clara, CA, USA), and Article Title: Breast Ultrasound Image Synthesis using Deep Convolutional Generative Adversarial Networks Article Snippet: Image synthesis was performed on DEEPstation DK-1000 (UEI, Tokyo, Japan) containing the graphics processing unit GeForce GTX 1080 (NVIDIA, CA, USA), central processing unit Core i7-8700 (Intel, CA, USA), and graphical user interface-based deep Article Title: Proposal to Improve the Image Quality of Short-Acquisition Time-Dedicated Breast Positron Emission Tomography Using the Pix2pix Generative Adversarial Network. Article Snippet: The computer (DEEPstation DK-1000; UEI, Tokyo, Japan) that was used in our image synthesis contained a graphic processing unit (GeForce GTX 1080; NVIDIA, Santa Clara, CA, USA), central processing unit (Core i7-8700; Intel, Santa Nlara, CA, USA), and a graphical user interface-based deep Construct:Article Title: Efficacy of endoscopic ultrasound with artificial intelligence for the diagnosis of gastrointestinal stromal tumors. Article Snippet: .. The EUS-AI was constructed on a DEEP station DK-1000 containing a GeForce GTX 1080 graphics processing unit (NVIDIA, California, USA), a Core i7-8700 central processing unit (Intel, California, USA), and a Deep Analyzer graphical user interface-based deep |