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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 391–400 of 436 papers

TitleStatusHype
MBDRes-U-Net: Multi-Scale Lightweight Brain Tumor Segmentation NetworkCode0
SuperLightNet: Lightweight Parameter Aggregation Network for Multimodal Brain Tumor SegmentationCode0
Utilizing Attention, Linked Blocks, And Pyramid Pooling To Propel Brain Tumor Segmentation In 3DCode0
Enhancing Brain Tumor Segmentation Using Channel Attention and Transfer learningCode0
Brain Tumor Segmentation using Enhanced U-Net Model with Empirical AnalysisCode0
Brain Tumor Segmentation Based on Refined Fully Convolutional Neural Networks with A Hierarchical Dice LossCode0
Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion SegmentationCode0
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithmCode0
3D-DDA: 3D Dual-Domain Attention for Brain Tumor SegmentationCode0
A Weakly Supervised and Globally Explainable Learning Framework for Brain Tumor SegmentationCode0
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