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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 201–210 of 436 papers

TitleStatusHype
High-Resolution Swin Transformer for Automatic Medical Image SegmentationCode1
Adaptive Active Contour Model for Brain Tumor SegmentationCode0
Single MR Image Super-Resolution using Generative Adversarial NetworkCode1
Brain MRI study for glioma segmentation using convolutional neural networks and original post-processing techniques with low computational demand—0
CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer with Modality-Correlated Cross-Attention for Brain Tumor Segmentation—0
TBraTS: Trusted Brain Tumor SegmentationCode1
Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation—0
CTVR-EHO TDA-IPH Topological Optimized Convolutional Visual Recurrent Network for Brain Tumor Segmentation and Classification—0
mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor SegmentationCode1
A Performance-Consistent and Computation-Efficient CNN System for High-Quality Automated Brain Tumor Segmentation—0
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