Improving Pediatric Low-Grade Neuroepithelial Tumors Molecular Subtype Identification Using a Novel AUROC Loss Function for Convolutional Neural Networks

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).

Generating 3D Brain Tumor Regions in MRI using Vector-Quantization Generative Adversarial Networks

no code implementations • 2 Oct 2023 • Meng Zhou, Matthias W Wagner, Uri Tabori, Cynthia Hawkins, Birgit B Ertl-Wagner, Farzad Khalvati

Research on deep learning-based brain tumor classification using MRI has shown that it is easier to classify the tumor ROIs compared to the entire image volumes.

Brain Tumor Segmentation Quantization +1

Generative Adversarial Networks for Weakly Supervised Generation and Evaluation of Brain Tumor Segmentations on MR Images

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.

Brain Tumor Segmentation Generative Adversarial Network +1

Improving Deep Learning Models for Pediatric Low-Grade Glioma Tumors Molecular Subtype Identification Using 3D Probability Distributions of Tumor Location

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.

Tumor Segmentation