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 / edited by Alessandro Crimi, Spyridon Bakas.

Format:

Book

Contributor:

Crimi, Alessandro, Editor.

Bakas, Spyridon, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 12659

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 12659

Language:

English

Subjects (All):

Computer vision.

Machine learning.

Pattern recognition systems.

Bioinformatics.

Computer Vision.

Machine Learning.

Automated Pattern Recognition.

Computational and Systems Biology.

Local Subjects:

Computer Vision.

Machine Learning.

Automated Pattern Recognition.

Computational and Systems Biology.

Physical Description:

1 online resource (XIX, 523 pages) : 25 illustrations

Edition:

1st ed. 2021.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2021.

System Details:

text file PDF

Summary:

This two-volume set LNCS 12658 and 12659 constitutes the thoroughly refereed proceedings of the 6th International MICCAI Brainlesion Workshop, BrainLes 2020, the International Multimodal Brain Tumor Segmentation (BraTS) challenge, and the Computational Precision Medicine: Radiology-Pathology Challenge on Brain Tumor Classification (CPM-RadPath) challenge. These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, in Lima, Peru, in October 2020.* The revised selected papers presented in these volumes were organized in the following topical sections: brain lesion image analysis (16 selected papers from 21 submissions); brain tumor image segmentation (69 selected papers from 75 submissions); and computational precision medicine: radiology-pathology challenge on brain tumor classification (6 selected papers from 6 submissions). *The workshop and challenges were held virtually.

Contents:

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 for Multimodal 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 Modified 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.

Other Format:

Printed edition:

ISBN:

978-3-030-72087-2

9783030720872

Access Restriction:

Restricted for use by site license.