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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 151–200 of 436 papers

TitleStatusHype
Adaptive Active Contour Model for Brain Tumor SegmentationCode0
Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance ImagingCode0
Multi-scale self-guided attention for medical image segmentationCode0
Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural NetworksCode0
Automatic brain tumor segmentation in 2D intra-operative ultrasound images using MRI tumor annotationsCode0
Decoupling Feature Representations of Ego and Other Modalities for Incomplete Multi-modal Brain Tumor SegmentationCode0
MBDRes-U-Net: Multi-Scale Lightweight Brain Tumor Segmentation NetworkCode0
Magnetic Resonance Imaging Feature-Based Subtyping and Model Ensemble for Enhanced Brain Tumor SegmentationCode0
MAProtoNet: A Multi-scale Attentive Interpretable Prototypical Part Network for 3D Magnetic Resonance Imaging Brain Tumor ClassificationCode0
A Weakly Supervised and Globally Explainable Learning Framework for Brain Tumor SegmentationCode0
Multi-step Cascaded Networks for Brain Tumor SegmentationCode0
Hybrid-Fusion Transformer for Multisequence MRICode0
HMM Model for Brain Tumor Detection and ClassificationCode0
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation ProblemsCode0
3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRICode0
3D MRI brain tumor segmentation using autoencoder regularizationCode0
Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain AdaptationCode0
Hypergraph Tversky-Aware Domain Incremental Learning for Brain Tumor Segmentation with Missing ModalitiesCode0
Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image SegmentationCode0
Autofocus Layer for Semantic SegmentationCode0
Generative Style Transfer for MRI Image Segmentation: A Case of Glioma Segmentation in Sub-Saharan AfricaCode0
Patient-Specific Real-Time Segmentation in Trackerless Brain UltrasoundCode0
A Multimodal Feature Distillation with CNN-Transformer Network for Brain Tumor Segmentation with Incomplete ModalitiesCode0
Glioma Segmentation with Cascaded UnetCode0
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS ChallengeCode0
FedRef: Communication-Efficient Bayesian Fine Tuning with Reference ModelCode0
FMG-Net and W-Net: Multigrid Inspired Deep Learning Architectures For Medical Imaging SegmentationCode0
CNN-based Segmentation of Medical Imaging DataCode0
FR-MRInet: A Deep Convolutional Encoder-Decoder for Brain Tumor Segmentation with Relu-RGB and Sliding-windowCode0
An Optimization Framework for Processing and Transfer Learning for the Brain Tumor SegmentationCode0
Feature Imitating Networks Enhance The Performance, Reliability And Speed Of Deep Learning On Biomedical Image Processing TasksCode0
Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion SegmentationCode0
Attention-Enhanced Hybrid Feature Aggregation Network for 3D Brain Tumor SegmentationCode0
A Tri-attention Fusion Guided Multi-modal Segmentation Network—0
CASPIANET++: A Multidimensional Channel-Spatial Asymmetric Attention Network with Noisy Student Curriculum Learning Paradigm for Brain Tumor Segmentation—0
Cascaded V-Net using ROI masks for brain tumor segmentation—0
A Transformer-based Generative Adversarial Network for Brain Tumor Segmentation—0
Building Brain Tumor Segmentation Networks with User-Assisted Filter Estimation and Selection—0
BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification with Swin-HAFNet—0
BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023—0
Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging—0
A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction—0
BraSyn 2023 challenge: Missing MRI synthesis and the effect of different learning objectives—0
A Structural Graph-Based Method for MRI Analysis—0
Brain Tumor Synthetic Segmentation in 3D Multimodal MRI Scans—0
Brain Tumor Survival Prediction using Radiomics Features—0
Brain tumor segmentation with missing modalities via latent multi-source correlation representation—0
ASC-Net: Unsupervised Medical Anomaly Segmentation Using an Adversarial-based Selective Cutting Network—0
A Feasibility study for Deep learning based automated brain tumor segmentation using Magnetic Resonance Images—0
Artificial Intelligence Solution for Effective Treatment Planning for Glioblastoma Patients—0
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