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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 401–436 of 436 papers

TitleStatusHype
Investigating certain choices of CNN configurations for brain lesion segmentation—0
Joint brain tumor segmentation from multi MR sequences through a deep convolutional neural network—0
KMD: Koopman Multi-modality Decomposition for Generalized Brain Tumor Segmentation under Incomplete Modalities—0
Knowledge distillation from multi-modal to mono-modal segmentation networks—0
Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalities—0
LATUP-Net: A Lightweight 3D Attention U-Net with Parallel Convolutions for Brain Tumor Segmentation—0
Learning Data Augmentation for Brain Tumor Segmentation with Coarse-to-Fine Generative Adversarial Networks—0
Learning Multi-Modal Brain Tumor Segmentation from Privileged Semi-Paired MRI Images with Curriculum Disentanglement Learning—0
Learning to Learn Unlearned Feature for Brain Tumor Segmentation—0
Leveraging SeNet and ResNet Synergy within an Encoder-Decoder Architecture for Glioma Detection—0
MAG-Net: Multi-task attention guided network for brain tumor segmentation and classification—0
Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation—0
Medical Image Analysis using Deep Relational Learning—0
Medical Transformer: Universal Brain Encoder for 3D MRI Analysis—0
MedMAP: Promoting Incomplete Multi-modal Brain Tumor Segmentation with Alignment—0
Memory Consistent Unsupervised Off-the-Shelf Model Adaptation for Source-Relaxed Medical Image Segmentation—0
Memory Efficient 3D U-Net with Reversible Mobile Inverted Bottlenecks for Brain Tumor Segmentation—0
ME-Net: Multi-Encoder Net Framework for Brain Tumor Segmentation—0
Mind the Gap: Promoting Missing Modality Brain Tumor Segmentation with Alignment—0
Modality-Aware and Shift Mixer for Multi-modal Brain Tumor Segmentation—0
Modality-Pairing Learning for Brain Tumor Segmentation—0
MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network—0
MRI brain tumor segmentation using informative feature vectors and kernel dictionary learning—0
MRI Brain Tumor Segmentation using Random Forests and Fully Convolutional Networks—0
Multi-class Brain Tumor Segmentation using Graph Attention Network—0
Multiclass MRI Brain Tumor Segmentation using 3D Attention-based U-Net—0
Multi-Domain Image Completion for Random Missing Input Data—0
Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation—0
Multimodal 3D Brain Tumor Segmentation with Adversarial Training and Conditional Random Field—0
Multi-modal Brain Tumor Segmentation via Missing Modality Synthesis and Modality-level Attention Fusion—0
Multi-Modal Brain Tumor Segmentation via 3D Multi-Scale Self-attention and Cross-attention—0
Multimodal CNN Networks for Brain Tumor Segmentation in MRI: A BraTS 2022 Challenge Solution—0
Multi Modal Convolutional Neural Networks for Brain Tumor Segmentation—0
Multimodal MRI brain tumor segmentation using random forests with features learned from fully convolutional neural network—0
Multimodal Self-Supervised Learning for Medical Image Analysis—0
Multi-Resolution 3D CNN for MRI Brain Tumor Segmentation and Survival Prediction—0
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