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Image Search Results


Journal: bioRxiv

Article Title: Stroke-Related Changes in Tonic and Phasic Muscle Recruitment During Reaching Reveal Pathway-Specific Motor Deficits

doi: 10.1101/2025.05.28.656732

Figure Lengend Snippet:

Article Snippet: To test these hypotheses, we fitted a non-parametric generalized linear mixed-effects (GLME) model ( fitglme function in Statistics and Machine Learning Toolbox, MATLAB) with an identity link and normal error distribution to examine the effects of three continuous predictors—Component (Tonic vs. Phasic), Direction (Outward vs. Inward), and Muscle Group (Proximal, Biarticular, vs. Distal)—on the sum of score tunings, along with their interactions.

Techniques:

Comparison between receiver-operating characteristics curves of the PTLN clinical-radiomics SVM, random forest and PTLN SVM, random forest. (A) The clinical-radiomics SVM model with Method 4 and BC16 showed a significantly higher AUC than the radiomics SVM model with Method 3 and BW128 (AUC 0.9775 vs. 0.9483). (B) The clinical-radiomics random forest model with Method 4 and BC32 exhibited a higher AUC than the radiomics random forest model with Method 4 and BC32 (AUC 0.9419 vs. 0.9231), although the difference was not significant.

Journal: Frontiers in Veterinary Science

Article Title: Computed tomography radiomics models of tumor differentiation in canine small intestinal tumors

doi: 10.3389/fvets.2024.1450304

Figure Lengend Snippet: Comparison between receiver-operating characteristics curves of the PTLN clinical-radiomics SVM, random forest and PTLN SVM, random forest. (A) The clinical-radiomics SVM model with Method 4 and BC16 showed a significantly higher AUC than the radiomics SVM model with Method 3 and BW128 (AUC 0.9775 vs. 0.9483). (B) The clinical-radiomics random forest model with Method 4 and BC32 exhibited a higher AUC than the radiomics random forest model with Method 4 and BC32 (AUC 0.9419 vs. 0.9231), although the difference was not significant.

Article Snippet: Radiomics models were constructed using the Statistics and Machine Learning Toolbox in MATLAB (MathWorks, Natick, MA, United States).

Techniques: Comparison

Comparison between receiver-operating characteristics curves of the PTLN clinical-radiomics SVM and random forest models. The SVM model with Method 4 and BC16 showed a significantly higher AUC than the random forest model with Method 4 and BC32 (AUC 0.9775 vs. 0.9412).

Journal: Frontiers in Veterinary Science

Article Title: Computed tomography radiomics models of tumor differentiation in canine small intestinal tumors

doi: 10.3389/fvets.2024.1450304

Figure Lengend Snippet: Comparison between receiver-operating characteristics curves of the PTLN clinical-radiomics SVM and random forest models. The SVM model with Method 4 and BC16 showed a significantly higher AUC than the random forest model with Method 4 and BC32 (AUC 0.9775 vs. 0.9412).

Article Snippet: Radiomics models were constructed using the Statistics and Machine Learning Toolbox in MATLAB (MathWorks, Natick, MA, United States).

Techniques: Comparison

Comparison among commonly selected radiomics features.

Journal: Frontiers in Veterinary Science

Article Title: Computed tomography radiomics models of tumor differentiation in canine small intestinal tumors

doi: 10.3389/fvets.2024.1450304

Figure Lengend Snippet: Comparison among commonly selected radiomics features.

Article Snippet: Radiomics models were constructed using the Statistics and Machine Learning Toolbox in MATLAB (MathWorks, Natick, MA, United States).

Techniques: Comparison