Search Results for author:
Found 13 papers, 0 papers with code
Improving Pediatric Low-Grade Neuroepithelial Tumors Molecular Subtype Identification Using a Novel AUROC Loss Function for Convolutional Neural Networks
The baseline model was trained using binary cross entropy (BCE), and achieved an AUROC of 86. 11% for differentiating BRAF fusion and BRAF V600E mutations, which was improved to 87. 71% using our proposed AUROC loss function (p-value 0. 045).
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).
Automating Cobb Angle Measurement for Adolescent Idiopathic Scoliosis using Instance Segmentation
This paper proposes to address the Cobb angle measurement task using YOLACT, an instance segmentation model.
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.
Improving Deep Learning Models for Pediatric Low-Grade Glioma Tumors Molecular Subtype Identification Using 3D Probability Distributions of Tumor Location
Conclusion: We achieved statistically significant improvements by incorporating tumor location into the CNN models.
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.
Minimizing the Effect of Noise and Limited Dataset Size in Image Classification Using Depth Estimation as an Auxiliary Task with Deep Multitask Learning
Generalizability is the ultimate goal of Machine Learning (ML) image classifiers, for which noise and limited dataset size are among the major concerns.
Open-radiomics: A Collection of Standardized Datasets and a Technical Protocol for Reproducible Radiomics Machine Learning Pipelines
Using PyRadiomics library for LGG vs. HGG classification, 288 radiomics datasets are formed; the combinations of 4 MRI sequences, 3 binWidths, 6 image normalization methods, and 4 tumor subregions.
A Transfer Learning Based Active Learning Framework for Brain Tumor Classification
The model achieved AUC of 82% compared with AUC of 78. 48% for the baseline, which reassures the robustness and stability of our proposed transfer learning augmented with active learning framework while significantly reducing the size of training data.
A Brief Review of Deep Multi-task Learning and Auxiliary Task Learning
Multi-task learning (MTL) optimizes several learning tasks simultaneously and leverages their shared information to improve generalization and the prediction of the model for each task.
A Modified AUC for Training Convolutional Neural Networks: Taking Confidence into Account
Receiver operating characteristic (ROC) curve is an informative tool in binary classification and Area Under ROC Curve (AUC) is a popular metric for reporting performance of binary classifiers.
A Comprehensive Study of Data Augmentation Strategies for Prostate Cancer Detection in Diffusion-weighted MRI using Convolutional Neural Networks
Data augmentation refers to a group of techniques whose goal is to battle limited amount of available data to improve model generalization and push sample distribution toward the true distribution.
Evolution-based Fine-tuning of CNNs for Prostate Cancer Detection
Convolutional Neural Networks (CNNs) have been used for automated detection of prostate cancer where Area Under Receiver Operating Characteristic (ROC) curve (AUC) is usually used as the performance metric.
