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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
Dealing with All-stage Missing Modality: Towards A Universal Model with Robust Reconstruction and Personalization—0
Decentralized Differentially Private Segmentation with PATE—0
Decentralized Gossip Mutual Learning (GML) for automatic head and neck tumor segmentation—0
Decentralized Gossip Mutual Learning (GML) for brain tumor segmentation on multi-parametric MRI—0
Decoupled Pyramid Correlation Network for Liver Tumor Segmentation from CT images—0
Deep and Statistical Learning in Biomedical Imaging: State of the Art in 3D MRI Brain Tumor Segmentation—0
Deep cross-modality (MR-CT) educed distillation learning for cone beam CT lung tumor segmentation—0
Deep Distance Map Regression Network with Shape-aware Loss for Imbalanced Medical Image Segmentation—0
Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings—0
Deepfake Image Generation for Improved Brain Tumor Segmentation—0
Radiologist-level Performance by Using Deep Learning for Segmentation of Breast Cancers on MRI Scans—0
Multimodal CNN Networks for Brain Tumor Segmentation in MRI: A BraTS 2022 Challenge Solution—0
Multi Modal Convolutional Neural Networks for Brain Tumor Segmentation—0
Multimodal Learning With Intraoperative CBCT & Variably Aligned Preoperative CT Data To Improve Segmentation—0
Multimodal MRI brain tumor segmentation using random forests with features learned from fully convolutional neural network—0
Multimodal Self-Supervised Learning for Medical Image Analysis—0
Multimodal Spatial Attention Module for Targeting Multimodal PET-CT Lung Tumor Segmentation—0
Multi-phase Liver Tumor Segmentation with Spatial Aggregation and Uncertain Region Inpainting—0
Multi-Resolution 3D CNN for MRI Brain Tumor Segmentation and Survival Prediction—0
Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation—0
Multi Scale Supervised 3D U-Net for Kidney and Tumor Segmentation—0
Multi-Slice Dense-Sparse Learning for Efficient Liver and Tumor Segmentation—0
Multi-stage Deep Layer Aggregation for Brain Tumor Segmentation—0
Multi-Task Generative Adversarial Network for Handling Imbalanced Clinical Data—0
Multi-task Learning To Improve Semantic Segmentation Of CBCT Scans Using Image Reconstruction—0
Multi-Threshold Attention U-Net (MTAU) based Model for Multimodal Brain Tumor Segmentation in MRI scans—0
Neural Network-Based Automatic Liver Tumor Segmentation With Random Forest-Based Candidate Filtering—0
Non Parametric Data Augmentations Improve Deep-Learning based Brain Tumor Segmentation—0
ONCOPILOT: A Promptable CT Foundation Model For Solid Tumor Evaluation—0
Optimizing Prediction of MGMT Promoter Methylation from MRI Scans using Adversarial Learning—0
Organ At Risk Segmentation with Multiple Modality—0
PAM-UNet: Shifting Attention on Region of Interest in Medical Images—0
Pancreatic Tumor Segmentation as Anomaly Detection in CT Images Using Denoising Diffusion Models—0
PA-ResSeg: A Phase Attention Residual Network for Liver Tumor Segmentation from Multi-phase CT Images—0
Segmentation of Parotid Gland Tumors Using Multimodal MRI and Contrastive Learning—0
Parotid Gland MRI Segmentation Based on Swin-Unet and Multimodal Images—0
Partial Labeled Gastric Tumor Segmentation via patch-based Reiterative Learning—0
PCA for Enhanced Cross-Dataset Generalizability in Breast Ultrasound Tumor Segmentation—0
PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training—0
PEMMA: Parameter-Efficient Multi-Modal Adaptation for Medical Image Segmentation—0
PINN-EMFNet: PINN-based and Enhanced Multi-Scale Feature Fusion Network for Breast Ultrasound Images Segmentation—0
Position Paper: Building Trust in Synthetic Data for Clinical AI—0
Predicting 1p19q Chromosomal Deletion of Low-Grade Gliomas from MR Images using Deep Learning—0
Predicting survival of glioblastoma from automatic whole-brain and tumor segmentation of MR images—0
Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learning—0
Prediction of Overall Survival of Brain Tumor Patients—0
PriorNet: lesion segmentation in PET-CT including prior tumor appearance information—0
propnet: Propagating 2D Annotation to 3D Segmentation for Gastric Tumors on CT Scans—0
PSO-UNet: Particle Swarm-Optimized U-Net Framework for Precise Multimodal Brain Tumor Segmentation—0
Quantitative Impact of Label Noise on the Quality of Segmentation of Brain Tumors on MRI scans—0
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