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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 351–400 of 436 papers

TitleStatusHype
Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings—0
Deepfake Image Generation for Improved Brain Tumor Segmentation—0
Deep Learning-Based Brain Image Segmentation for Automated Tumour Detection—0
Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluation—0
Deep Learning with Mixed Supervision for Brain Tumor Segmentation—0
Deep Recurrent Level Set for Segmenting Brain Tumors—0
Detection of Under-represented Samples Using Dynamic Batch Training for Brain Tumor Segmentation from MR Images—0
Differential Privacy for Adaptive Weight Aggregation in Federated Tumor Segmentation—0
DIGEST: Deeply supervIsed knowledGE tranSfer neTwork learning for brain tumor segmentation with incomplete multi-modal MRI scans—0
Dilated Inception U-Net (DIU-Net) for Brain Tumor Segmentation—0
Disentangled Multimodal Brain MR Image Translation via Transformer-based Modality Infuser—0
Does anatomical contextual information improve 3D U-Net based brain tumor segmentation?—0
Domain Game: Disentangle Anatomical Feature for Single Domain Generalized Segmentation—0
Efficient Brain Tumor Segmentation Using a Dual-Decoder 3D U-Net with Attention Gates (DDUNet)—0
Efficient embedding network for 3D brain tumor segmentation—0
Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentation—0
Empirical Evaluation of the Segment Anything Model (SAM) for Brain Tumor Segmentation—0
End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks—0
Enhancing Brain Tumor Classification Using TrAdaBoost and Multi-Classifier Deep Learning Approaches—0
Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation—0
Enhancing MRI Brain Tumor Segmentation with an Additional Classification Network—0
Ensemble Learning with Residual Transformer for Brain Tumor Segmentation—0
Experimenting with Knowledge Distillation techniques for performing Brain Tumor Segmentation—0
Exploring SAM Ablations for Enhancing Medical Segmentation in Radiology and Pathology—0
Feature-enhanced Generation and Multi-modality Fusion based Deep Neural Network for Brain Tumor Segmentation with Missing MR Modalities—0
Federated brain tumor segmentation: an extensive benchmark—0
Few-Shot Generation of Brain Tumors for Secure and Fair Data Sharing—0
Flexible Fusion Network for Multi-modal Brain Tumor Segmentation—0
Focus, Segment and Erase: An Efficient Network for Multi-Label Brain Tumor Segmentation—0
Fully Automated Tumor Segmentation for Brain MRI data using Multiplanner UNet—0
Fully Automatic Brain Tumor Segmentation using a Normalized Gaussian Bayesian Classifier and 3D Fluid Vector Flow—0
GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models—0
Generating 3D Brain Tumor Regions in MRI using Vector-Quantization Generative Adversarial Networks—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
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