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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 401–450 of 786 papers

TitleStatusHype
Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentation—0
PA-Seg: Learning from Point Annotations for 3D Medical Image Segmentation using Contextual Regularization and Cross Knowledge DistillationCode1
Analyzing Deep Learning Based Brain Tumor Segmentation with Missing MRI Modalities—0
Deep Learning and Health Informatics for Smart Monitoring and Diagnosis—0
Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Image Segmentation—0
A Transformer-based Generative Adversarial Network for Brain Tumor Segmentation—0
PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training—0
High-Resolution Swin Transformer for Automatic Medical Image SegmentationCode1
Adaptive Active Contour Model for Brain Tumor SegmentationCode0
Large-Kernel Attention for 3D Medical Image Segmentation—0
Single MR Image Super-Resolution using Generative Adversarial NetworkCode1
Brain MRI study for glioma segmentation using convolutional neural networks and original post-processing techniques with low computational demand—0
CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer with Modality-Correlated Cross-Attention for Brain Tumor Segmentation—0
MMOTU: A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic SegmentationCode1
Slice-by-slice deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for spatial uncertainty on FDG PET and CT images—0
TBraTS: Trusted Brain Tumor SegmentationCode1
Free-form Lesion Synthesis Using a Partial Convolution Generative Adversarial Network for Enhanced Deep Learning Liver Tumor Segmentation—0
Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation—0
Parotid Gland MRI Segmentation Based on Swin-Unet and Multimodal Images—0
CTVR-EHO TDA-IPH Topological Optimized Convolutional Visual Recurrent Network for Brain Tumor Segmentation and Classification—0
mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor SegmentationCode1
Decoupled Pyramid Correlation Network for Liver Tumor Segmentation from CT images—0
FedMix: Mixed Supervised Federated Learning for Medical Image SegmentationCode1
A Performance-Consistent and Computation-Efficient CNN System for High-Quality Automated Brain Tumor Segmentation—0
Preoperative brain tumor imaging: models and software for segmentation and standardized reportingCode1
Class Balanced PixelNet for Neurological Image Segmentation—0
Multi-View Hypercomplex Learning for Breast Cancer ScreeningCode1
Negligible effect of brain MRI data preprocessing for tumor segmentationCode0
SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalitiesCode1
UNetFormer: A Unified Vision Transformer Model and Pre-Training Framework for 3D Medical Image SegmentationCode3
Category Guided Attention Network for Brain Tumor Segmentation in MRICode0
Translation Consistent Semi-supervised Segmentation for 3D Medical ImagesCode1
ME-Net: Multi-Encoder Net Framework for Brain Tumor Segmentation—0
Slice Imputation: Intermediate Slice Interpolation for Anisotropic 3D Medical Image Segmentation—0
Self Pre-training with Masked Autoencoders for Medical Image Classification and SegmentationCode1
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
Conquering Data Variations in Resolution: A Slice-Aware Multi-Branch Decoder Network—0
Joint brain tumor segmentation from multi MR sequences through a deep convolutional neural network—0
Factorizer: A Scalable Interpretable Approach to Context Modeling for Medical Image SegmentationCode1
Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor SegmentationCode0
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
A Data Augmentation Method for Fully Automatic Brain Tumor Segmentation—0
HNF-Netv2 for Brain Tumor Segmentation using multi-modal MR Imaging—0
TransBTSV2: Towards Better and More Efficient Volumetric Segmentation of Medical ImagesCode2
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
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