Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm (2019-06-01T00:00:00.000000Z)

TL;DR

A fully automatic way to quantify tumor imaging characteristics using deep learning-based segmentation and test whether these characteristics are predictive of tumor genomic subtypes is proposed.

Authors

M. Mazurowski

3 papers

Mateusz Buda

2 papers

Ashirbani Saha

3 papers

TL;DR

Authors

References38 items

Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI

Brain Tumor Segmentation Based on Refined Fully Convolutional Neural Networks with A Hierarchical Dice Loss

Image Segmentation with Pyramid Dilated Convolution Based on ResNet and U-Net

A systematic study of the class imbalance problem in convolutional neural networks

Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations

Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation using Holistic Convolutional Networks

Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions

Radiogenomics of lower-grade glioma: algorithmically-assessed tumor shape is associated with tumor genomic subtypes and patient outcomes in a multi-institutional study with The Cancer Genome Atlas data

VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images

Deep Learning Advances in Computer Vision with 3D Data

Radiogenomic analysis of lower grade glioma: a pilot multi-institutional study shows an association between quantitative image features and tumor genomics

A survey on deep learning in medical image analysis

The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation

The Importance of Skip Connections in Biomedical Image Segmentation

Deep Learning Trends for Focal Brain Pathology Segmentation in MRI

3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation

V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

Genomic profiling of lower-grade gliomas uncovers cohesive disease groups: implications for diagnosis and treatment

Rethinking the Inception Architecture for Computer Vision

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

Radiogenomics: what it is and why it is important.

Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas.

Glioma Groups Based on 1p/19q, IDH, and TERT Promoter Mutations in Tumors.

U-Net: Convolutional Networks for Biomedical Image Segmentation

Learning Deconvolution Network for Semantic Segmentation

Brain tumor segmentation with Deep Neural Networks

Deep learning of representations and its application to computer vision

Fully convolutional networks for semantic segmentation

ImageNet classification with deep convolutional neural networks

Deep Sparse Rectifier Neural Networks

Multi-scaled morphological features for the characterization of mammographic masses using statistical classification schemes

Convolutional networks for images, speech, and time series

Quantitative classification of breast tumors in digitized mammograms.

Computerized characterization of mammographic masses: analysis of spiculation.

Measures of the Amount of Ecologic Association Between Species

Semantic Segmentation with

Gradient-based learning applied to document recognition

Field of Study

Journal Information

Name

Page

Volume