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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

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Showing 361–370 of 436 papers

TitleStatusHype
Hypergraph Tversky-Aware Domain Incremental Learning for Brain Tumor Segmentation with Missing ModalitiesCode0
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS ChallengeCode0
Parameter-efficient Fine-tuning for improved Convolutional Baseline for Brain Tumor Segmentation in Sub-Saharan Africa Adult Glioma DatasetCode0
CNN-based Segmentation of Medical Imaging DataCode0
Patient-Specific Real-Time Segmentation in Trackerless Brain UltrasoundCode0
Automatic brain tumor segmentation in 2D intra-operative ultrasound images using MRI tumor annotationsCode0
Category Guided Attention Network for Brain Tumor Segmentation in MRICode0
Autofocus Layer for Semantic SegmentationCode0
Post-hoc Overall Survival Time Prediction from Brain MRICode0
Attention-Enhanced Hybrid Feature Aggregation Network for 3D Brain Tumor SegmentationCode0
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