base cnn (SoftMax Inc)

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/result/base cnn/product/SoftMax Inc
Average 90 stars, based on 1 article reviews

base cnn - by Bioz Stars, 2026-05

90/100 stars

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base cnn (Kaggle Inc)

Kaggle Inc base cnn

<t>Base</t> <t>CNN</t> model architecture.

Base Cnn, supplied by Kaggle 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/result/base cnn/product/Kaggle Inc
Average 90 stars, based on 1 article reviews

base cnn - by Bioz Stars, 2026-05

90/100 stars

Buy from Supplier

Image Search Results

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:

Journal: Food Science & Nutrition

Article Title: Enhanced Maize Leaf Disease Detection and Classification Using an Integrated CNN ‐ ViT Model

doi: 10.1002/fsn3.70513

Figure Lengend Snippet: Base CNN model architecture.

Article Snippet: Base CNN , Kaggle + Mendeley , Fold 1 , 97.85 , 97.80 , 97.76 , 97.78.

Techniques:

Results of the base CNN model: (a) Training and validation loss and (b) training and validation accuracy.

Journal: Food Science & Nutrition

Article Title: Enhanced Maize Leaf Disease Detection and Classification Using an Integrated CNN ‐ ViT Model

doi: 10.1002/fsn3.70513

Figure Lengend Snippet: Results of the base CNN model: (a) Training and validation loss and (b) training and validation accuracy.

Article Snippet: Base CNN , Kaggle + Mendeley , Fold 1 , 97.85 , 97.80 , 97.76 , 97.78.

Techniques: Biomarker Discovery

Development of performance metrics for the base CNN model.

Journal: Food Science & Nutrition

Article Title: Enhanced Maize Leaf Disease Detection and Classification Using an Integrated CNN ‐ ViT Model

doi: 10.1002/fsn3.70513

Figure Lengend Snippet: Development of performance metrics for the base CNN model.

Article Snippet: Base CNN , Kaggle + Mendeley , Fold 1 , 97.85 , 97.80 , 97.76 , 97.78.

Techniques:

Confusion matrix for the base CNN model.

Journal: Food Science & Nutrition

Article Title: Enhanced Maize Leaf Disease Detection and Classification Using an Integrated CNN ‐ ViT Model

doi: 10.1002/fsn3.70513

Figure Lengend Snippet: Confusion matrix for the base CNN model.

Article Snippet: Base CNN , Kaggle + Mendeley , Fold 1 , 97.85 , 97.80 , 97.76 , 97.78.

Techniques:

Journal: Food Science & Nutrition

Article Title: Enhanced Maize Leaf Disease Detection and Classification Using an Integrated CNN ‐ ViT Model

doi: 10.1002/fsn3.70513

Figure Lengend Snippet: ROC curve for the base CNN model.

Article Snippet: Base CNN , Kaggle + Mendeley , Fold 1 , 97.85 , 97.80 , 97.76 , 97.78.

Techniques:

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