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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
Selective Complementary Feature Fusion and Modal Feature Compression Interaction for Brain Tumor SegmentationCode0
Task-oriented Uncertainty Collaborative Learning for Label-Efficient Brain Tumor SegmentationCode0
GBT-SAM: Adapting a Foundational Deep Learning Model for Generalizable Brain Tumor Segmentation via Efficient Integration of Multi-Parametric MRI DataCode1
Clinical Inspired MRI Lesion Segmentation0
Synthetic Poisoning Attacks: The Impact of Poisoned MRI Image on U-Net Brain Tumor Segmentation0
Position Paper: Building Trust in Synthetic Data for Clinical AI0
Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings0
CLISC: Bridging clip and sam by enhanced cam for unsupervised brain tumor segmentationCode1
AMM-Diff: Adaptive Multi-Modality Diffusion Network for Missing Modality Imputation0
Enhancing Brain Tumor Segmentation Using Channel Attention and Transfer learningCode0
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