deep cnn Search Results


region based deep cnn  (IEEE Access)
90
IEEE Access region based deep cnn
Region Based Deep 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
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deep cnn  (Aiforia Technologies)
90
Aiforia Technologies deep cnn
Deep Cnn, supplied by Aiforia Technologies, 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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deep convolutional neural network (cnn)-based encoding model  (Murty Pharmaceuticals)
90
Murty Pharmaceuticals deep convolutional neural network (cnn)-based encoding model
Deep Convolutional Neural Network (Cnn) Based Encoding Model, supplied by Murty Pharmaceuticals, 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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deep cnn neuro-t version 2.0.1  (Straumann GmbH)
90
Straumann GmbH deep cnn neuro-t version 2.0.1
Deep Cnn Neuro T Version 2.0.1, supplied by Straumann 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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deep convolutional neural network  (Becton Dickinson)
90
Becton Dickinson deep convolutional neural network
Deep Convolutional Neural Network, supplied by Becton Dickinson, 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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cnn-catboost ensemble deep learning model  (Sahyadri Hospitals Ltd)
90
Sahyadri Hospitals Ltd cnn-catboost ensemble deep learning model
Cnn Catboost Ensemble Deep Learning Model, supplied by Sahyadri Hospitals Ltd, 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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deep cnn  (Becton Dickinson)
90
Becton Dickinson deep cnn
Overview of AI Applications in COVID-19 Diagnosis and Detection.
Deep Cnn, supplied by Becton Dickinson, 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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cnn deep residual learning  (EyePACS LLC)
90
EyePACS LLC cnn deep residual learning
Overview of AI Applications in COVID-19 Diagnosis and Detection.
Cnn Deep Residual Learning, supplied by EyePACS LLC, 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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deep convolutional neural network (cnn) classifier, anoxpepred  (HealthTech Connex Inc)
90
HealthTech Connex Inc deep convolutional neural network (cnn) classifier, anoxpepred
Overview of AI Applications in COVID-19 Diagnosis and Detection.
Deep Convolutional Neural Network (Cnn) Classifier, Anoxpepred, supplied by HealthTech Connex 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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mask r-cnn deep learning model  (Esri inc)
90
Esri inc mask r-cnn deep learning model
Overview of AI Applications in COVID-19 Diagnosis and Detection.
Mask R Cnn Deep Learning Model, supplied by Esri 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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multiresolution deep cnn  (Panoptes Pharma GmbH)
90
Panoptes Pharma GmbH multiresolution deep cnn
Working of PANOPTES, a <t>multiresolution</t> deep <t>convolutional</t> <t>neural</t> <t>network</t> <t>(CNN).</t> In this trained model generated through the CNN algorithm, pathological images were used to predict the gene mutations and histological and molecular subtypes of EC. PANOPTES ML models using multiresolution architecture can classify histological subtypes of EC, molecular subtypes, and critical mutations (loss or gain of functions) with decent performance based on H&E (hematoxylin and eosin), IHC (immunohistochemistry), and IF (immunofluorescence) images. Also, some data can be accessed from the input CEL files. Predicated on the input, CNN models identify subtypes and mutations in EC. Multiresolution CNN models outperform single-resolution CNN models on the visual patterns. CNN models would incorporate human interpretable tumor characteristics according to feature extraction. Tumor grade identifies the molecular subtype and classifies into high-risk or low-risk cohorts from endometrioid histology samples to capture characteristics of varied sizes on the H&E, IHC, and IF slides, which is similar to a human operator pathological evaluation. Unlike traditional CNN architectures, Panoptes’ input is a group of three tiles from the same region on the image slide rather than one tile. CEL file: differential expression profile generated by Affymetrix DNA microarray-based software analysis.
Multiresolution Deep Cnn, 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
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active deep learning cnn architecture  (EyePACS LLC)
90
EyePACS LLC active deep learning cnn architecture
Working of PANOPTES, a <t>multiresolution</t> deep <t>convolutional</t> <t>neural</t> <t>network</t> <t>(CNN).</t> In this trained model generated through the CNN algorithm, pathological images were used to predict the gene mutations and histological and molecular subtypes of EC. PANOPTES ML models using multiresolution architecture can classify histological subtypes of EC, molecular subtypes, and critical mutations (loss or gain of functions) with decent performance based on H&E (hematoxylin and eosin), IHC (immunohistochemistry), and IF (immunofluorescence) images. Also, some data can be accessed from the input CEL files. Predicated on the input, CNN models identify subtypes and mutations in EC. Multiresolution CNN models outperform single-resolution CNN models on the visual patterns. CNN models would incorporate human interpretable tumor characteristics according to feature extraction. Tumor grade identifies the molecular subtype and classifies into high-risk or low-risk cohorts from endometrioid histology samples to capture characteristics of varied sizes on the H&E, IHC, and IF slides, which is similar to a human operator pathological evaluation. Unlike traditional CNN architectures, Panoptes’ input is a group of three tiles from the same region on the image slide rather than one tile. CEL file: differential expression profile generated by Affymetrix DNA microarray-based software analysis.
Active Deep Learning Cnn Architecture, supplied by EyePACS LLC, 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/active deep learning cnn architecture/product/EyePACS LLC
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active deep learning cnn architecture - by Bioz Stars, 2026-05
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Image Search Results


Overview of AI Applications in COVID-19 Diagnosis and Detection.

Journal: Microorganisms

Article Title: The Impact of Artificial Intelligence on Microbial Diagnosis

doi: 10.3390/microorganisms12061051

Figure Lengend Snippet: Overview of AI Applications in COVID-19 Diagnosis and Detection.

Article Snippet: Additionally, Becton Dickinson Kiestra has developed a deep CNN that captures images of urine samples for culture analysis [ ].

Techniques: Biomarker Discovery, Fluorescence, Imaging, Infection, Clinical Proteomics, Extraction, Diagnostic Assay

Working of PANOPTES, a multiresolution deep convolutional neural network (CNN). In this trained model generated through the CNN algorithm, pathological images were used to predict the gene mutations and histological and molecular subtypes of EC. PANOPTES ML models using multiresolution architecture can classify histological subtypes of EC, molecular subtypes, and critical mutations (loss or gain of functions) with decent performance based on H&E (hematoxylin and eosin), IHC (immunohistochemistry), and IF (immunofluorescence) images. Also, some data can be accessed from the input CEL files. Predicated on the input, CNN models identify subtypes and mutations in EC. Multiresolution CNN models outperform single-resolution CNN models on the visual patterns. CNN models would incorporate human interpretable tumor characteristics according to feature extraction. Tumor grade identifies the molecular subtype and classifies into high-risk or low-risk cohorts from endometrioid histology samples to capture characteristics of varied sizes on the H&E, IHC, and IF slides, which is similar to a human operator pathological evaluation. Unlike traditional CNN architectures, Panoptes’ input is a group of three tiles from the same region on the image slide rather than one tile. CEL file: differential expression profile generated by Affymetrix DNA microarray-based software analysis.

Journal: Frontiers in Oncology

Article Title: Machine Learning for Endometrial Cancer Prediction and Prognostication

doi: 10.3389/fonc.2022.852746

Figure Lengend Snippet: Working of PANOPTES, a multiresolution deep convolutional neural network (CNN). In this trained model generated through the CNN algorithm, pathological images were used to predict the gene mutations and histological and molecular subtypes of EC. PANOPTES ML models using multiresolution architecture can classify histological subtypes of EC, molecular subtypes, and critical mutations (loss or gain of functions) with decent performance based on H&E (hematoxylin and eosin), IHC (immunohistochemistry), and IF (immunofluorescence) images. Also, some data can be accessed from the input CEL files. Predicated on the input, CNN models identify subtypes and mutations in EC. Multiresolution CNN models outperform single-resolution CNN models on the visual patterns. CNN models would incorporate human interpretable tumor characteristics according to feature extraction. Tumor grade identifies the molecular subtype and classifies into high-risk or low-risk cohorts from endometrioid histology samples to capture characteristics of varied sizes on the H&E, IHC, and IF slides, which is similar to a human operator pathological evaluation. Unlike traditional CNN architectures, Panoptes’ input is a group of three tiles from the same region on the image slide rather than one tile. CEL file: differential expression profile generated by Affymetrix DNA microarray-based software analysis.

Article Snippet: In a recent development in EC diagnosis, a multiresolution deep CNN named “Panoptes” was utilized where pathological images were used to predict the gene mutations and histological and molecular subtypes in EC ( ) ( ).

Techniques: Generated, Immunohistochemistry, Immunofluorescence, Extraction, Quantitative Proteomics, Microarray, Software