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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 8190 of 436 papers

TitleStatusHype
Unveiling Incomplete Modality Brain Tumor Segmentation: Leveraging Masked Predicted Auto-Encoder and Divergence Learning0
Interactive Image Selection and Training for Brain Tumor Segmentation Network0
Domain Game: Disentangle Anatomical Feature for Single Domain Generalized Segmentation0
Dealing with All-stage Missing Modality: Towards A Universal Model with Robust Reconstruction and Personalization0
Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation0
The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI0
SiNGR: Brain Tumor Segmentation via Signed Normalized Geodesic Transform RegressionCode1
Hybrid Multihead Attentive Unet-3D for Brain Tumor Segmentation0
Patient-Specific Real-Time Segmentation in Trackerless Brain UltrasoundCode0
Meta-Learned Modality-Weighted Knowledge Distillation for Robust Multi-Modal Learning with Missing DataCode0
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