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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 501–525 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
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