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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 301–350 of 436 papers

TitleStatusHype
SAR: Scale-Aware Restoration Learning for 3D Tumor Segmentation—0
Unsupervised Brain Tumor Segmentation with Image-based Prompts—0
Consistent estimation of the max-flow problem: Towards unsupervised image segmentation—0
Unsupervised Region-based Anomaly Detection in Brain MRI with Adversarial Image Inpainting—0
Unveiling Incomplete Modality Brain Tumor Segmentation: Leveraging Masked Predicted Auto-Encoder and Divergence Learning—0
Using Singular Value Decomposition in a Convolutional Neural Network to Improve Brain Tumor Segmentation Accuracy—0
Using U-Net Network for Efficient Brain Tumor Segmentation in MRI Images—0
VIViT: Variable-Input Vision Transformer Framework for 3D MR Image Segmentation—0
Within-Brain Classification for Brain Tumor Segmentation—0
Glioblastoma Multiforme Patient Survival Prediction—0
Global Planar Convolutions for improved context aggregation in Brain Tumor Segmentation—0
H2NF-Net for Brain Tumor Segmentation using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task—0
Here Comes the Explanation: A Shapley Perspective on Multi-contrast Medical Image Segmentation—0
Hierarchical multi-class segmentation of glioma images using networks with multi-level activation function—0
HI-Net: Hyperdense Inception 3D UNet for Brain Tumor Segmentation—0
HNF-Netv2 for Brain Tumor Segmentation using multi-modal MR Imaging—0
Hybrid Multihead Attentive Unet-3D for Brain Tumor Segmentation—0
Image-level supervision and self-training for transformer-based cross-modality tumor segmentation—0
Impact of Spherical Coordinates Transformation Pre-processing in Deep Convolution Neural Networks for Brain Tumor Segmentation and Survival Prediction—0
Improving 3D U-Net for Brain Tumor Segmentation by Utilizing Lesion Prior—0
Improving the Segmentation of Pediatric Low-Grade Gliomas through Multitask Learning—0
Incomplete Multi-modal Brain Tumor Segmentation via Learnable Sorting State Space Model—0
Incremental Learning for Heterogeneous Structure Segmentation in Brain Tumor MRI—0
Integrating Edges into U-Net Models with Explainable Activation Maps for Brain Tumor Segmentation using MR Images—0
Interactive Image Selection and Training for Brain Tumor Segmentation Network—0
Intraoperative Glioma Segmentation with YOLO + SAM for Improved Accuracy in Tumor Resection—0
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
Generative Style Transfer for MRI Image Segmentation: A Case of Glioma Segmentation in Sub-Saharan AfricaCode0
3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRICode0
Glioma Segmentation with Cascaded UnetCode0
On Enhancing Brain Tumor Segmentation Across Diverse Populations with Convolutional Neural NetworksCode0
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