Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

Invited Papers.- Glioma Diagnosis and Classification: Illuminating the Gold Standard.- Multiple Sclerosis Lesion Segmentation - A Survey of Supervised CNN-Based Methods.- Computational Diagnostics of GBM Tumors in the Era of Radiomics and Radiogenomics.- Brain Lesion Image Analysis.- Automatic Segmentation of Non-Tumor Tissues in Glioma MR Brain Images Using Deformable Registration with Partial Convolutional Networks.- Convolutional neural network with asymmetric encoding and decoding structure for brain vessel segmentation on computed tomographic angiography.- Volume Preserving Brain Lesion Segmentation.- Microstructural modulations in the hippocampus allow to characterizing relapsing-remitting versus primary progressive multiple sclerosis.- Symmetric-Constrained Irregular Structure Inpainting for Brain MRI Registration with Tumor Pathology.- Multivariate analysis is sufficient for lesion-behaviour mapping.- Label-Efficient Multi-Task Segmentation using Contrastive Learning.- Spatio-temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation.- MMSSD: Multi-scale and Multi-level Single Shot Detector for Brain Metastases Detection.- Unsupervised 3D Brain Anomaly Detection.- Assessing Lesion Segmentation Bias of Neural Networks on Motion Corrupted Brain MRI Tejas Sudharshan Mathai, Yi Wang, Nathan Cross.- Estimating Glioblastoma Biophysical Growth Parameters Using Deep Learning Regression.- Bayesian Skip Net: Building on Prior Information for the Prediction and Segmentation of Stroke Lesions.- Brain Tumor Segmentation.- Brain Tumor Segmentation Using Dual-Path Attention U-net in 3D MRI Images.- Multimodal Brain Image Analysis and Survival Prediction.- Using Neuromorphic Attention-based Neural Networks.- Context Aware 3D UNet for Brain Tumor Segmentation.- Modality-Pairing Learning for Brain Tumor Segmentation.- Transfer Learning for Brain Tumor Segmentation.- Efficient embedding network for 3D brain tumor segmentation.- Segmentation of the multimodal brain tumor images used Res-U-Net.- Vox2Vox: 3D-GAN for Brain Tumour Segmentation.- Automatic Brain Tumor Segmentation with Scale Attention Network.- Impact of Spherical Coordinates Transformation Pre-processing in Deep Convolution Neural Networks for Brain Tumor Segmentation and Survival Prediction.- Overall Survival Prediction for Glioblastoma on Pre-Treatment MRI Using Robust Radiomics and Priors.- Glioma segmentation using encoder-decoder network and survival prediction based on cox analysis.- Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution.- Brain tumour segmentation using a triplanar ensemble of U-Nets on MR images.- MRI brain tumor segmentation using a 2D-3D U-Net ensemble.- Multimodal Brain Tumor Segmentation and Survival Prediction Using a 3D Self-Ensemble ResUNet.- MRI Brain Tumor Segmentation and Uncertainty Estimation using 3D-UNet architectures.- Utility of Brain Parcellation in Enhancing Brain Tumor Segmentation and Survival Prediction.- Uncertainty-driven refinement of tumor core segmentation using 3D-to-2D networks with label uncertainty.- Multi-Decoder Networks with Multi-Denoising Inputs for Tumor Segmentation.- MultiATTUNet: Brain Tumor Segmentation and Survival Multitasking.- A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor Segmentation.- Ensemble of Two Dimensional Networks for Bain Tumor Segmentation.- Cascaded Coarse-to-Fine Neural Network for Brain Tumor Segmentation.- Low-Rank Convolutional Networks for Brain Tumor Segmentation.- Brain tumour segmentation using cascaded 3D densely-connected U-net.- Segmentation then Prediction: A Multi-task Solution to Brain Tumor Segmentation and Survival Prediction.- Enhancing MRI Brain Tumor Segmentation with an Additional Classification Network.- Self-training for Brain Tumour Segmentation with Uncertainty Estimation and Biophysics-Guided Survival Prediction.