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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

Showing 2130 of 436 papers

TitleStatusHype
3D Self-Supervised Methods for Medical ImagingCode1
Brain Tumor Segmentation and Survival Prediction using 3D Attention UNetCode1
Rethinking Brain Tumor Segmentation from the Frequency Domain PerspectiveCode1
CANet: Context Aware Network for 3D Brain Glioma SegmentationCode1
CLISC: Bridging clip and sam by enhanced cam for unsupervised brain tumor segmentationCode1
cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image SynthesisCode1
Deep Learning-Based Concurrent Brain Registration and Tumor SegmentationCode1
DeepSeg: Deep Neural Network Framework for Automatic Brain Tumor Segmentation using Magnetic Resonance FLAIR ImagesCode1
A Joint Graph and Image Convolution Network for Automatic Brain Tumor SegmentationCode1
Attention-Guided Version of 2D UNet for Automatic Brain Tumor SegmentationCode1
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