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Brain Tumor Segmentation

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 )

Papers

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Showing 301–310 of 436 papers

TitleStatusHype
Conditional generator and multi-sourcecorrelation guided brain tumor segmentation with missing MR modalities—0
Brain tumour segmentation using a triplanar ensemble of U-NetsCode0
Experimenting with Knowledge Distillation techniques for performing Brain Tumor Segmentation—0
Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain AdaptationCode0
Medical Transformer: Universal Brain Encoder for 3D MRI Analysis—0
Memory Efficient 3D U-Net with Reversible Mobile Inverted Bottlenecks for Brain Tumor Segmentation—0
Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalities—0
Transfer learning for automatic brain tumor classification Using MRI Images.—0
Deep and Statistical Learning in Biomedical Imaging: State of the Art in 3D MRI Brain Tumor Segmentation—0
Post-hoc Overall Survival Time Prediction from Brain MRICode0
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