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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 121130 of 436 papers

TitleStatusHype
Multi-scale self-guided attention for medical image segmentationCode0
Generative Style Transfer for MRI Image Segmentation: A Case of Glioma Segmentation in Sub-Saharan AfricaCode0
Hypergraph Tversky-Aware Domain Incremental Learning for Brain Tumor Segmentation with Missing ModalitiesCode0
A Novel Confidence Induced Class Activation Mapping for MRI Brain Tumor SegmentationCode0
Brain Tumor Detection using Convolutional Neural NetworkCode0
Feature Imitating Networks Enhance The Performance, Reliability And Speed Of Deep Learning On Biomedical Image Processing TasksCode0
FedRef: Communication-Efficient Bayesian Fine Tuning with Reference ModelCode0
Exploiting Partial Common Information Microstructure for Multi-Modal Brain Tumor SegmentationCode0
An Optimization Framework for Processing and Transfer Learning for the Brain Tumor SegmentationCode0
Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor SegmentationCode0
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