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Tumor Location-weighted MRI-Report Contrastive Learning: A Framework for Improving the Explainability of Pediatric Brain Tumor Diagnosis
Despite the promising performance of convolutional neural networks (CNNs) in brain tumor diagnosis from magnetic resonance imaging (MRI), their integration into the clinical workflow has been limited.
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).
Generating 3D Brain Tumor Regions in MRI using Vector-Quantization Generative Adversarial Networks
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.
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.
