cnn based system Search Results


cad based on cnn  (HUMAN Gesellschaft)
90
HUMAN Gesellschaft cad based on cnn
Summary of existing works related to ML and DL techniques for medical imaging
Cad Based On Cnn, supplied by HUMAN Gesellschaft, 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/cnn+based+system/pmc09788870-30-8-24?v=HUMAN+Gesellschaft
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domain fusion cnn-lstm  (IEEE Access)
90
IEEE Access domain fusion cnn-lstm
Summary of existing works related to ML and DL techniques for medical imaging
Domain Fusion Cnn Lstm, 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
https://www.bioz.com/product/cnn+based+system/10__1109_slash_ACCESS__2021__3107954-396-8-19?v=IEEE+Access
Average 90 stars, based on 1 article reviews
domain fusion cnn-lstm - by Bioz Stars, 2026-07
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region based deep cnn  (IEEE Access)
90
IEEE Access region based deep cnn
Summary of existing works related to ML and DL techniques for medical imaging
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
https://www.bioz.com/product/cnn+based+system/10__1109_slash_access__2021__3063716-472-18-26?v=IEEE+Access
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cnn-based plant health detection  (Hydrofarm Inc)
90
Hydrofarm Inc cnn-based plant health detection
Summary of existing works related to ML and DL techniques for medical imaging
Cnn Based Plant Health Detection, supplied by Hydrofarm 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/cnn+based+system/pmc12190697-148-3-1?v=Hydrofarm+Inc
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auto-zooming cnn-based framework for real-time pedestrian detection in outdoor surveillance videos  (IEEE Access)
90
IEEE Access auto-zooming cnn-based framework for real-time pedestrian detection in outdoor surveillance videos
Summary of existing works related to ML and DL techniques for medical imaging
Auto Zooming Cnn Based Framework For Real Time Pedestrian Detection In Outdoor Surveillance Videos, 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
https://www.bioz.com/product/cnn+based+system/ppr0594671-190-11-25?v=IEEE+Access
Average 90 stars, based on 1 article reviews
auto-zooming cnn-based framework for real-time pedestrian detection in outdoor surveillance videos - by Bioz Stars, 2026-07
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base cnn  (SoftMax Inc)
90
SoftMax Inc base cnn
Proposed lesion quantification framework, shown with the liver MRI as an example. First a <t>base</t> <t>CNN</t> is trained with a training set consisting of multiple patients. Next, the base CNN is refined in the patient-specific FT step using a previous MRI exam of a patient (the baseline scan). The fine-tuned CNN is used to detect or segment lesions in a follow-up MRI scan of the same patient. The images are cropped to focus of the organ of interest. The cropped image size is 128 × 128 pixels .
Base Cnn, supplied by SoftMax 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/cnn+based+system/pmc07744252-238-5-1?v=SoftMax+Inc
Average 90 stars, based on 1 article reviews
base cnn - by Bioz Stars, 2026-07
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webcam-based eye movement analysis using cnn  (IEEE Access)
90
IEEE Access webcam-based eye movement analysis using cnn
Proposed lesion quantification framework, shown with the liver MRI as an example. First a <t>base</t> <t>CNN</t> is trained with a training set consisting of multiple patients. Next, the base CNN is refined in the patient-specific FT step using a previous MRI exam of a patient (the baseline scan). The fine-tuned CNN is used to detect or segment lesions in a follow-up MRI scan of the same patient. The images are cropped to focus of the organ of interest. The cropped image size is 128 × 128 pixels .
Webcam Based Eye Movement Analysis Using 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
https://www.bioz.com/product/cnn+based+system/10__1109_slash_tnsre__2024__3496087-348-4-12?v=IEEE+Access
Average 90 stars, based on 1 article reviews
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cnn and inception-v4 network-based software variseetm  (EyePACS LLC)
90
EyePACS LLC cnn and inception-v4 network-based software variseetm
A comparison of reported deep learning techniques for screening and recognition.
Cnn And Inception V4 Network Based Software Variseetm, 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/product/cnn+based+system/pmc09505428-0-13-31?v=EyePACS+LLC
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cnn and inception-v4 network-based software variseetm - by Bioz Stars, 2026-07
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cnn-based ai tool  (Panoptes Pharma GmbH)
90
Panoptes Pharma GmbH cnn-based ai tool
A comparison of reported deep learning techniques for screening and recognition.
Cnn Based Ai Tool, 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
https://www.bioz.com/product/cnn+based+system/pmc12025868-133-6-3?v=Panoptes+Pharma+GmbH
Average 90 stars, based on 1 article reviews
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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
A comparison of reported deep learning techniques for screening and recognition.
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
https://www.bioz.com/product/cnn+based+system/pm32916543-360-8-17?v=IEEE+Access
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panoptes-based multi-resolution cnn models  (Panoptes Pharma GmbH)
90
Panoptes Pharma GmbH panoptes-based multi-resolution cnn models
A comparison of reported deep learning techniques for screening and recognition.
Panoptes Based Multi Resolution Cnn Models, 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
https://www.bioz.com/product/cnn+based+system/pmc10518635-300-0-0?v=Panoptes+Pharma+GmbH
Average 90 stars, based on 1 article reviews
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character based cnn-bidirectional lstm-crf model  (Mainichi Newspapers Co)
90
Mainichi Newspapers Co character based cnn-bidirectional lstm-crf model
A comparison of reported deep learning techniques for screening and recognition.
Character Based Cnn Bidirectional Lstm Crf Model, supplied by Mainichi Newspapers Co, 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/cnn+based+system/pmc06315256-78-10-18?v=Mainichi+Newspapers+Co
Average 90 stars, based on 1 article reviews
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Image Search Results


Summary of existing works related to ML and DL techniques for medical imaging

Journal: Multimedia Tools and Applications

Article Title: Machine learning and deep learning approach for medical image analysis: diagnosis to detection

doi: 10.1007/s11042-022-14305-w

Figure Lengend Snippet: Summary of existing works related to ML and DL techniques for medical imaging

Article Snippet: 5 , [ ] , 2020 , ● CAD based on CNN , ● AUC ● Sensitivity ● Specificity ● Multiview features ● Five human diagnostics , ● Breast cancer classification (benign and malignant) , ● Sensitivity: 88.6% ● Specificity: 87.6% ● AUC: 0.9468.

Techniques: Selection, Labeling, Functional Assay, Imaging, Modification, Computed Tomography, Biomarker Discovery, Extraction

Proposed lesion quantification framework, shown with the liver MRI as an example. First a base CNN is trained with a training set consisting of multiple patients. Next, the base CNN is refined in the patient-specific FT step using a previous MRI exam of a patient (the baseline scan). The fine-tuned CNN is used to detect or segment lesions in a follow-up MRI scan of the same patient. The images are cropped to focus of the organ of interest. The cropped image size is 128 × 128 pixels .

Journal: Journal of Medical Imaging

Article Title: Patient-specific fine-tuning of convolutional neural networks for follow-up lesion quantification

doi: 10.1117/1.JMI.7.6.064003

Figure Lengend Snippet: Proposed lesion quantification framework, shown with the liver MRI as an example. First a base CNN is trained with a training set consisting of multiple patients. Next, the base CNN is refined in the patient-specific FT step using a previous MRI exam of a patient (the baseline scan). The fine-tuned CNN is used to detect or segment lesions in a follow-up MRI scan of the same patient. The images are cropped to focus of the organ of interest. The cropped image size is 128 × 128 pixels .

Article Snippet: The Softmax probabilities of the base CNN had a mean maximum SD of 0.398 ( ± 0.025 ).

Techniques:

Median (IQR) of the TPR, FPC, and F1 score of the liver metastases detection for a varying number of iterations of learning for the CNN for FT. The best results are printed in bold.

Journal: Journal of Medical Imaging

Article Title: Patient-specific fine-tuning of convolutional neural networks for follow-up lesion quantification

doi: 10.1117/1.JMI.7.6.064003

Figure Lengend Snippet: Median (IQR) of the TPR, FPC, and F1 score of the liver metastases detection for a varying number of iterations of learning for the CNN for FT. The best results are printed in bold.

Article Snippet: The Softmax probabilities of the base CNN had a mean maximum SD of 0.398 ( ± 0.025 ).

Techniques:

Median (IQR) of the TPR, FPC, and F1 score for a ranging number of slices presented to the CNN for FT. The best results are printed in bold. No significant differences were found between the Base CNN and all options.

Journal: Journal of Medical Imaging

Article Title: Patient-specific fine-tuning of convolutional neural networks for follow-up lesion quantification

doi: 10.1117/1.JMI.7.6.064003

Figure Lengend Snippet: Median (IQR) of the TPR, FPC, and F1 score for a ranging number of slices presented to the CNN for FT. The best results are printed in bold. No significant differences were found between the Base CNN and all options.

Article Snippet: The Softmax probabilities of the base CNN had a mean maximum SD of 0.398 ( ± 0.025 ).

Techniques:

Median (IQR) of the TPR, the FPC and the F1 score of the liver metastases detection, for weighting the true positives, false negatives, and false positives during the patient-specific FT. The best results are printed in bold.

Journal: Journal of Medical Imaging

Article Title: Patient-specific fine-tuning of convolutional neural networks for follow-up lesion quantification

doi: 10.1117/1.JMI.7.6.064003

Figure Lengend Snippet: Median (IQR) of the TPR, the FPC and the F1 score of the liver metastases detection, for weighting the true positives, false negatives, and false positives during the patient-specific FT. The best results are printed in bold.

Article Snippet: The Softmax probabilities of the base CNN had a mean maximum SD of 0.398 ( ± 0.025 ).

Techniques:

Examples of the detection results on the follow-up scan of the base CNN and the patient-specific CNN for three different patients. White outline = manual annotation, red outline = false positive object, green check = detected metastasis, red cross = missed metastasis.

Journal: Journal of Medical Imaging

Article Title: Patient-specific fine-tuning of convolutional neural networks for follow-up lesion quantification

doi: 10.1117/1.JMI.7.6.064003

Figure Lengend Snippet: Examples of the detection results on the follow-up scan of the base CNN and the patient-specific CNN for three different patients. White outline = manual annotation, red outline = false positive object, green check = detected metastasis, red cross = missed metastasis.

Article Snippet: The Softmax probabilities of the base CNN had a mean maximum SD of 0.398 ( ± 0.025 ).

Techniques:

Mean ( ± SD ) of the Dice score and AVD of the WMH segmentation for a varying number of slices for FT. The best results are printed in bold.

Journal: Journal of Medical Imaging

Article Title: Patient-specific fine-tuning of convolutional neural networks for follow-up lesion quantification

doi: 10.1117/1.JMI.7.6.064003

Figure Lengend Snippet: Mean ( ± SD ) of the Dice score and AVD of the WMH segmentation for a varying number of slices for FT. The best results are printed in bold.

Article Snippet: The Softmax probabilities of the base CNN had a mean maximum SD of 0.398 ( ± 0.025 ).

Techniques:

Mean ( ± SD ) of the Dice score and AVD of the WMH segmentation for weighting the true positives, false negatives, and false positives during the patient-specific FT. The best results are printed in bold.

Journal: Journal of Medical Imaging

Article Title: Patient-specific fine-tuning of convolutional neural networks for follow-up lesion quantification

doi: 10.1117/1.JMI.7.6.064003

Figure Lengend Snippet: Mean ( ± SD ) of the Dice score and AVD of the WMH segmentation for weighting the true positives, false negatives, and false positives during the patient-specific FT. The best results are printed in bold.

Article Snippet: The Softmax probabilities of the base CNN had a mean maximum SD of 0.398 ( ± 0.025 ).

Techniques:

Examples of the follow-up scan with the segmentation results of the base CNN and the patient-specific CNN for three different patients. Green = true positive pixels, red = false negative pixels, and blue = false positive pixels.

Journal: Journal of Medical Imaging

Article Title: Patient-specific fine-tuning of convolutional neural networks for follow-up lesion quantification

doi: 10.1117/1.JMI.7.6.064003

Figure Lengend Snippet: Examples of the follow-up scan with the segmentation results of the base CNN and the patient-specific CNN for three different patients. Green = true positive pixels, red = false negative pixels, and blue = false positive pixels.

Article Snippet: The Softmax probabilities of the base CNN had a mean maximum SD of 0.398 ( ± 0.025 ).

Techniques:

An example of the uncertainty (SD of Softmax probability) of the base CNN and the patient-specific CNN. A high SD means the CNN is uncertain about its decision.

Journal: Journal of Medical Imaging

Article Title: Patient-specific fine-tuning of convolutional neural networks for follow-up lesion quantification

doi: 10.1117/1.JMI.7.6.064003

Figure Lengend Snippet: An example of the uncertainty (SD of Softmax probability) of the base CNN and the patient-specific CNN. A high SD means the CNN is uncertain about its decision.

Article Snippet: The Softmax probabilities of the base CNN had a mean maximum SD of 0.398 ( ± 0.025 ).

Techniques:

A comparison of reported deep learning techniques for screening and recognition.

Journal: Sensors (Basel, Switzerland)

Article Title: Deep Learning for Diabetic Retinopathy Analysis: A Review, Research Challenges, and Future Directions

doi: 10.3390/s22186780

Figure Lengend Snippet: A comparison of reported deep learning techniques for screening and recognition.

Article Snippet: Yi-Ting Hsieh et al. [ ] , CNN and Inception-V4 network-based software named VariSeeTM , Not Mentioned , Custom-developed at National Taiwan University Hospital between July 2007 and June 2017 + EyePACS , 39,136 , Color fundus images , Maximum accuracy = 98.4%.

Techniques: Software