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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 51–60 of 436 papers

TitleStatusHype
Enhancing Brain Tumor Classification Using TrAdaBoost and Multi-Classifier Deep Learning Approaches—0
AEPL: Automated and Editable Prompt Learning for Brain Tumor Segmentation—0
Segmentation of Pediatric Brain Tumors using a Radiologically informed, Deep Learning Cascade—0
Two-Stage Approach for Brain MR Image Synthesis: 2D Image Synthesis and 3D Refinement—0
Federated brain tumor segmentation: an extensive benchmark—0
Optimizing Medical Image Segmentation with Advanced Decoder DesignCode0
Enhanced MRI brain tumor detection and classification via topological data analysis and low-rank tensor decompositionCode1
Mind the Gap: Promoting Missing Modality Brain Tumor Segmentation with Alignment—0
Multiscale Encoder and Omni-Dimensional Dynamic Convolution Enrichment in nnU-Net for Brain Tumor SegmentationCode1
multiPI-TransBTS: A Multi-Path Learning Framework for Brain Tumor Image Segmentation Based on Multi-Physical InformationCode0
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