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 II 1st ed. 2021 [Minkštas viršelis]

Brain Tumor Segmentation.- Lightweight U-Nets for Brain Tumor
Segmentation.- Efficient Brain Tumour Segmentation using Co-registered Data
and Ensembles of Specialised Learners.- Efficient MRI Brain Tumor
Segmentation using Multi-Resolution Encoder-Decoder Networks.- Trialing U-Net
Training Modifications for Segmenting Gliomas Using Open Source Deep Learning
Framework.- HI-Net: Hyperdense Inception 3D UNet for Brain Tumor
Segmentation.- H2NF-Net for Brain Tumor Segmentation using Multimodal MR
Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task.- 2D
Dense-UNet: A Clinically Valid Approach to Automated Glioma Segmentation.-
Attention U-Net with Dimension-hybridized Fast Data Density Functional Theory
for Automatic Brain Tumor Image Segmentation.- MVP U-Net: Multi-View
Pointwise U-Net for Brain Tumor Segmentation.- Glioma Segmentation with 3D
U-Net Backed with Energy- Based Post- Processing.- nnU-Net for Brain Tumor
Segmentation.- A Deep Random Forest Approach forMultimodal Brain Tumor
Segmentation.- Brain tumor segmentation and associated uncertainty evaluation
using Multi-sequences MRI Mixture Data Preprocessing.- A Deep supervision CNN
network for Brain tumor Segmentation.- Multi-Threshold Attention U-Net (MTAU)
based Model for Multimodal Brain Tumor Segmentation in MRI scans.-
Multi-stage Deep Layer Aggregation for Brain Tumor Segmentation.- Glioma
Segmentation Using Ensemble of 2D/3D U-Nets and Survival Prediction Using
Multiple Features Fusion.- Generalized Wasserstein Dice Score,
Distributionally Robust Deep Learning, and Ranger for brain tumor
segmentation: BraTS 2020 challenge.- 3D Semantic Segmentation of Brain Tumor
for Overall Survival Prediction.- Segmentation, Survival Prediction, and
Uncertainty Estimation of Gliomas from Multimodal 3D MRI using Selective
Kernel Networks.- 3D brain tumor segmentation and survival prediction using
ensembles of Convolutional Neural Networks.- Brain Tumour Segmentation using
Probabilistic U-Net.- Segmenting Brain Tumors from MRI Using Cascaded 3D
U-Nets.- A Deep Supervised U-Attention Net for Pixel-wise Brain Tumor
Segmentation.- A two stage atrous convolution neural network for brain tumor
segmentation.- TwoPath U-Net for Automatic Brain Tumor Segmentation from
Multimodal MRI data.- Brain Tumor Segmentation and Survival Prediction using
Automatic Hardmining in 3D CNN Architecture.- Some New Tricks for Deep Glioma
Segmentation.- PieceNet: A Redundant UNet Ensemble.- Cerberus: A Multi-headed
Network for BrainTumor Segmentation.- An Automatic Overall Survival Time
Prediction System for Glioma Brain Tumor Patients based on Volumetric and
Shape Features.- Squeeze-and-Excitation Normalization for Brain Tumor
Segmentation.- Modified MobileNet for Patient Survival Prediction.- Memory
Efficient 3D U-Net with Reversible Mobile Inverted Bottlenecks for Brain
Tumor Segmentation.- Brain Tumor Segmentation and Survival Prediction Using
Patch Based Modied U-Net.- DR-Unet104 for Multimodal MRI brain tumor 
segmentation.- Glioma Sub-region Segmentation on Multi-parameter MRI with
Label Dropout.- Variational-Autoencoder Regularized 3D MultiResUNet for the
BraTS 2020 Brain Tumor Segmentation.- Learning Dynamic Convolutions for
Multi-Modal 3D MRI Brain Tumor Segmentation.- Computational Precision
Medicine: Radiology-Pathology Challenge on Brain Tumor Classification.-
Automatic Glioma Grading Based on Two-stage Networks by Integrating Pathology
and MRI Images.- Brain Tumor Classification Based on MRI Images and Noise
Reduced Pathology Images.- Multimodal brain tumor classification.- A Hybrid
Convolutional Neural Network Based-Method for Brain Tumor Classification
Using mMRI and WSI.- CNN-based Fully Automatic Glioma Classification with
Multi-modal Medical Images.- Glioma Classification Using Multimodal Radiology
and Histology Data.