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Found 3 papers, 0 papers with code
Non-invasive Liver Fibrosis Screening on CT Images using Radiomics
The combination of hyperparameters and features that yielded the highest AUC was a logistic regression model with inputs features of maximum, energy, kurtosis, skewness, and small area high gray level emphasis extracted from non-contrast enhanced NC CT normalized using Gamma correction with $\gamma$ = 1. 5 (AUC, 0. 7833; 95% CI: 0. 7821, 0. 7845), (sensitivity, 0. 9091; 95% CI: 0. 9091, 0. 9091).
Generative Adversarial Networks for Weakly Supervised Generation and Evaluation of Brain Tumor Segmentations on MR Images
Segmentation of regions of interest (ROIs) for identifying abnormalities is a leading problem in medical imaging.
Deep Superpixel Generation and Clustering for Weakly Supervised Segmentation of Brain Tumors in MR Images
Training machine learning models to segment tumors and other anomalies in medical images is an important step for developing diagnostic tools but generally requires manually annotated ground truth segmentations, which necessitates significant time and resources.
