Journal: Computational Intelligence and Neuroscience

Article Title: Motion Intent Recognition in Intelligent Lower Limb Prosthesis Using One-Dimensional Dual-Tree Complex Wavelet Transforms

doi: 10.1155/2021/5631730

Figure Lengend Snippet: Comparison of our method with other methods with the same data set under user-dependent classification.

Article Snippet: Ten able-bodied , Healthy side , Self-selection feature of CNN , Softmax , 5 , 8 , 94.15%±3.04%.

Techniques: Comparison, Extraction

Journal: Sensors (Basel, Switzerland)

Article Title: An Efficient Machine Learning-Based Emotional Valence Recognition Approach Towards Wearable EEG

doi: 10.3390/s23031255

Figure Lengend Snippet: Summary of EEG-based emotion recognition approaches that utilize the DEAP dataset.

Article Snippet: Cui et al., 2020 [ ] , Symmetric Channels , All except delta , Regional-Asymmetric CNN (RACNN) , Softmax , Dep. , Val. Arl. , 96.65 97.11.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: An Efficient Machine Learning-Based Emotional Valence Recognition Approach Towards Wearable EEG

doi: 10.3390/s23031255

Figure Lengend Snippet: Valence (happy/sad) classification performance for the DEAP dataset.

Article Snippet: Cui et al., 2020 [ ] , Symmetric Channels , All except delta , Regional-Asymmetric CNN (RACNN) , Softmax , Dep. , Val. Arl. , 96.65 97.11.

Techniques:

Journal: Diagnostic Pathology

Article Title: Microscopic nuclei classification, segmentation, and detection with improved deep convolutional neural networks (DCNN)

doi: 10.1186/s13000-022-01189-5

Figure Lengend Snippet: Nuclei classification accuracy and comparison against other machine learning and deep learning methods

Article Snippet: SoftMax CNN + NEP [ ] , 0.784 , 0.917.

Techniques: Comparison

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:

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:

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:

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:

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:

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:

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:

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:

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: