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Tumor Segmentation

Tumor Segmentation is the task of identifying the spatial location of a tumor. It is a pixel-level prediction where each pixel is classified as a tumor or background. The most popular benchmark for this task is the BraTS dataset. The models are typically evaluated with the Dice Score metric.

Papers

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Showing 501–550 of 786 papers

TitleStatusHype
A Performance-Consistent and Computation-Efficient CNN System for High-Quality Automated Brain Tumor Segmentation—0
Class Balanced PixelNet for Neurological Image Segmentation—0
Negligible effect of brain MRI data preprocessing for tumor segmentationCode0
Category Guided Attention Network for Brain Tumor Segmentation in MRICode0
Slice Imputation: Intermediate Slice Interpolation for Anisotropic 3D Medical Image Segmentation—0
ME-Net: Multi-Encoder Net Framework for Brain Tumor Segmentation—0
Artificial Intelligence Solution for Effective Treatment Planning for Glioblastoma Patients—0
Multi-modal Brain Tumor Segmentation via Missing Modality Synthesis and Modality-level Attention Fusion—0
Joint brain tumor segmentation from multi MR sequences through a deep convolutional neural network—0
Conquering Data Variations in Resolution: A Slice-Aware Multi-Branch Decoder Network—0
Segmentation and Risk Score Prediction of Head and Neck Cancers in PET/CT Volumes with 3D U-Net and Cox Proportional Hazard Neural Networks—0
Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor SegmentationCode0
A Data Augmentation Method for Fully Automatic Brain Tumor Segmentation—0
HNF-Netv2 for Brain Tumor Segmentation using multi-modal MR Imaging—0
Joint Liver and Hepatic Lesion Segmentation in MRI using a Hybrid CNN with Transformer Layers—0
SoftDropConnect (SDC) -- Effective and Efficient Quantification of the Network Uncertainty in Deep MR Image AnalysisCode0
Automatic Segmentation of Head and Neck Tumor: How Powerful Transformers Are?Code0
Self-semantic contour adaptation for cross modality brain tumor segmentation—0
Optimizing Prediction of MGMT Promoter Methylation from MRI Scans using Adversarial Learning—0
Cross-Modality Deep Feature Learning for Brain Tumor Segmentation—0
United adversarial learning for liver tumor segmentation and detection of multi-modality non-contrast MRI—0
Lung-Originated Tumor Segmentation from Computed Tomography Scan (LOTUS) Benchmark—0
Brain Tumor Classification by Cascaded Multiscale Multitask Learning Framework Based on Feature Aggregation—0
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking ResultsCode0
ASC-Net: Unsupervised Medical Anomaly Segmentation Using an Adversarial-based Selective Cutting Network—0
Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation—0
Leveraging Human Selective Attention for Medical Image Analysis with Limited Training Data—0
Improving the Segmentation of Pediatric Low-Grade Gliomas through Multitask Learning—0
Exploiting full Resolution Feature Context for Liver Tumor and Vessel Segmentation via Integrate Framework: Application to Liver Tumor and Vessel 3D Reconstruction under embedded microprocessorCode0
Non Parametric Data Augmentations Improve Deep-Learning based Brain Tumor Segmentation—0
Exploring Feature Representation Learning for Semi-supervised Medical Image SegmentationCode0
Segmentation of Lung Tumor from CT Images using Deep Supervision—0
FedCostWAvg: A new averaging for better Federated Learning—0
Feature-enhanced Generation and Multi-modality Fusion based Deep Neural Network for Brain Tumor Segmentation with Missing MR Modalities—0
A Tri-attention Fusion Guided Multi-modal Segmentation Network—0
Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIs—0
Correlation between image quality metrics of magnetic resonance images and the neural network segmentation accuracy—0
Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image SegmentationCode0
Combining CNNs With Transformer for Multimodal 3D MRI Brain Tumor Segmentation With Self-Supervised Pretraining—0
Optimized U-Net for Brain Tumor SegmentationCode0
A transformer-based deep learning approach for classifying brain metastases into primary organ sites using clinical whole brain MRICode0
A Prior Knowledge Based Tumor and Tumoral Subregion Segmentation Tool for Pediatric Brain Tumors—0
Self-Supervised Learning for 3D Medical Image Analysis using 3D SimCLR and Monte Carlo Dropout—0
Utilizing Attention, Linked Blocks, And Pyramid Pooling To Propel Brain Tumor Segmentation In 3DCode0
All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation—0
Predicting survival of glioblastoma from automatic whole-brain and tumor segmentation of MR images—0
Leveraging Clinical Characteristics for Improved Deep Learning-Based Kidney Tumor Segmentation on CT—0
Self-supervised Tumor Segmentation through Layer Decomposition—0
An End-to-End learnable Flow Regularized Model for Brain Tumor Segmentation—0
Evaluating Transformer-based Semantic Segmentation Networks for Pathological Image Segmentation—0
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