Brain Tumor Segmentation
58 papers with code • 8 benchmarks • 5 datasets
Brain tumor segmentation is the task of segmenting tumors from other brain artefacts in MRI image of the brain.
( Image credit: Brain Tumor Segmentation with Deep Neural Networks )
Benchmarks
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BraTS 2018
BraTS 2017
BraTS 2015
BraTS 2013
BraTS 2014
Greatest papers with code
nnU-Net for Brain Tumor Segmentation
MIC-DKFZ/nnunet
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We apply nnU-Net to the segmentation task of the BraTS 2020 challenge.
Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation
We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation.
Ranked #1 on
Lesion Segmentation
on ISLES-2015
3D Medical Imaging Segmentation
Brain Lesion Segmentation From Mri
+2
3D MRI brain tumor segmentation using autoencoder regularization
black0017/MedicalZooPytorch
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Automated segmentation of brain tumors from 3D magnetic resonance images (MRIs) is necessary for the diagnosis, monitoring, and treatment planning of the disease.
Ranked #1 on
Brain Tumor Segmentation
on BRATS 2018
Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis
More importantly, learning a model from scratch simply in 3D may not necessarily yield performance better than transfer learning from ImageNet in 2D, but our Models Genesis consistently top any 2D approaches including fine-tuning the models pre-trained from ImageNet as well as fine-tuning the 2D versions of our Models Genesis, confirming the importance of 3D anatomical information and significance of our Models Genesis for 3D medical imaging.
Ranked #1 on
Lung Nodule Segmentation
on LIDC-IDRI
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm
mateuszbuda/brain-segmentation-pytorch
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Based on automatic deep learning segmentations, we extracted three features which quantify two-dimensional and three-dimensional characteristics of the tumors.
Ranked #2 on
Brain Segmentation
on Brain MRI segmentation
Multi-scale self-guided attention for medical image segmentation
sinAshish/Multi-Scale-Attention
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arXiv preprint 2019
In this paper we attempt to overcome these limitations with the proposed architecture, by capturing richer contextual dependencies based on the use of guided self-attention mechanisms.
Ranked #1 on
Medical Image Segmentation
on HSVM
Brain Tumor Segmentation with Deep Neural Networks
Finally, we explore a cascade architecture in which the output of a basic CNN is treated as an additional source of information for a subsequent CNN.
Ranked #1 on
Brain Tumor Segmentation
on BRATS-2013 leaderboard
KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation
jeya-maria-jose/KiU-Net-pytorch
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To overcome this issue, we propose using an overcomplete convolutional architecture where we project our input image into a higher dimension such that we constrain the receptive field from increasing in the deep layers of the network.
Ranked #1 on
Medical Image Segmentation
on RITE
Autofocus Layer for Semantic Segmentation
We propose the autofocus convolutional layer for semantic segmentation with the objective of enhancing the capabilities of neural networks for multi-scale processing.
Ranked #3 on
Brain Tumor Segmentation
on BRATS-2015
Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge
pykao/Modified-3D-UNet-Pytorch
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Quantitative analysis of brain tumors is critical for clinical decision making.
Ranked #2 on
Brain Tumor Segmentation
on BRATS-2015