El. knyga: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers, Part I

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.