medical-7
Brain Tumor Segmentation
3260 papers • 126 benchmarks • 313 datasets
Brain Tumor Segmentation is a medical image analysis task that involves the separation of brain tumors from normal brain tissue in magnetic resonance imaging (MRI) scans. The goal of brain tumor segmentation is to produce a binary or multi-class segmentation map that accurately reflects the location and extent of the tumor. ( Image credit: Brain Tumor Segmentation with Deep Neural Networks )
(Image credit: Papersgraph)
Benchmarks
These leaderboards are used to track progress in brain-tumor-segmentation-14
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BRATS-2015
BRATS-2015
BRATS-2013
BRATS-2013
BRATS 2018
BRATS 2018
Libraries
iUse these libraries to find brain-tumor-segmentation-14 models and implementations
2 papers 1,979
Datasets
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Most implemented papers
Attention U-Net: Learning Where to Look for the Pancreas
D. Rueckert, Nils Y. Hammerla, Ben Glocker, M. Heinrich, O. Oktay, Jo Schlemper, Steven G. McDonagh, Loic Le Folgoc, M. J. Lee, K. Misawa, K. Mori, Bernhard Kainz•Tue Apr 10 2018
A novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes is proposed to eliminate the necessity of using explicit external tissue/organ localisation modules of cascaded convolutional neural networks (CNNs).
