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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 91100 of 436 papers

TitleStatusHype
E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image SegmentationCode1
HMM Model for Brain Tumor Detection and ClassificationCode0
Generative Style Transfer for MRI Image Segmentation: A Case of Glioma Segmentation in Sub-Saharan AfricaCode0
Glioma Segmentation with Cascaded UnetCode0
Hybrid-Fusion Transformer for Multisequence MRICode0
AGD-Autoencoder: Attention Gated Deep Convolutional Autoencoder for Brain Tumor SegmentationCode0
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithmCode0
A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor SegmentationCode0
3D-DDA: 3D Dual-Domain Attention for Brain Tumor SegmentationCode0
FedRef: Communication-Efficient Bayesian Fine Tuning with Reference ModelCode0
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