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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 371380 of 436 papers

TitleStatusHype
FedRef: Communication-Efficient Bayesian Fine Tuning with Reference ModelCode0
UKAN-EP: Enhancing U-KAN with Efficient Attention and Pyramid Aggregation for 3D Multi-Modal MRI Brain Tumor SegmentationCode0
Promptable Counterfactual Diffusion Model for Unified Brain Tumor Segmentation and Generation with MRIsCode0
3D U-Net Based Brain Tumor Segmentation and Survival Days PredictionCode0
Feature Imitating Networks Enhance The Performance, Reliability And Speed Of Deep Learning On Biomedical Image Processing TasksCode0
Whole-brain radiomics for clustered federated personalization in brain tumor segmentationCode0
Exploiting Partial Common Information Microstructure for Multi-Modal Brain Tumor SegmentationCode0
QCResUNet: Joint Subject-level and Voxel-level Segmentation Quality PredictionCode0
AGD-Autoencoder: Attention Gated Deep Convolutional Autoencoder for Brain Tumor SegmentationCode0
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking ResultsCode0
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