no code implementations • 5 Feb 2024 • Khashayar Namdar, Matthias W. Wagner, Cynthia Hawkins, Uri Tabori, Birgit B. Ertl-Wagner, Farzad Khalvati
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).
no code implementations • 25 Nov 2022 • Jay J. Yoo, Khashayar Namdar, Sean Carey, Sandra E. Fischer, Chris McIntosh, Farzad Khalvati, Patrik Rogalla
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).
no code implementations • 25 Nov 2022 • Chaojun Chen, Khashayar Namdar, Yujie Wu, Shahob Hosseinpour, Manohar Shroff, Andrea S. Doria, Farzad Khalvati
This paper proposes to address the Cobb angle measurement task using YOLACT, an instance segmentation model.
no code implementations • 10 Nov 2022 • Jay J. Yoo, Khashayar Namdar, Matthias W. Wagner, Liana Nobre, Uri Tabori, Cynthia Hawkins, Birgit B. Ertl-Wagner, Farzad Khalvati
Segmentation of regions of interest (ROIs) for identifying abnormalities is a leading problem in medical imaging.
no code implementations • 13 Oct 2022 • Khashayar Namdar, Matthias W. Wagner, Kareem Kudus, Cynthia Hawkins, Uri Tabori, Brigit Ertl-Wagner, Farzad Khalvati
Conclusion: We achieved statistically significant improvements by incorporating tumor location into the CNN models.
no code implementations • 20 Sep 2022 • Jay J. Yoo, Khashayar Namdar, Farzad Khalvati
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.
no code implementations • 22 Aug 2022 • Khashayar Namdar, Partoo Vafaeikia, Farzad Khalvati
Generalizability is the ultimate goal of Machine Learning (ML) image classifiers, for which noise and limited dataset size are among the major concerns.
no code implementations • 29 Jul 2022 • Khashayar Namdar, Matthias W. Wagner, Birgit B. Ertl-Wagner, Farzad Khalvati
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.
no code implementations • 16 Nov 2020 • Ruqian Hao, Khashayar Namdar, Lin Liu, Farzad Khalvati
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.
no code implementations • 2 Jul 2020 • Partoo Vafaeikia, Khashayar Namdar, Farzad Khalvati
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.
no code implementations • 8 Jun 2020 • Khashayar Namdar, Masoom A. Haider, Farzad Khalvati
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.
no code implementations • 1 Jun 2020 • Ruqian Hao, Khashayar Namdar, Lin Liu, Masoom A. Haider, Farzad Khalvati
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.
no code implementations • 4 Nov 2019 • Khashayar Namdar, Isha Gujrathi, Masoom A. Haider, Farzad Khalvati
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.