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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 551–600 of 786 papers

TitleStatusHype
Trustworthy Multi-phase Liver Tumor Segmentation via Evidence-based Uncertainty—0
Tumor-Centered Patching for Enhanced Medical Image Segmentation—0
A Segmentation Foundation Model for Diverse-type Tumors—0
3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training—0
Optimizing Prediction of MGMT Promoter Methylation from MRI Scans using Adversarial Learning—0
Improving Deep Learning Models for Pediatric Low-Grade Glioma Tumors Molecular Subtype Identification Using 3D Probability Distributions of Tumor Location—0
Organ At Risk Segmentation with Multiple Modality—0
ASC-Net: Unsupervised Medical Anomaly Segmentation Using an Adversarial-based Selective Cutting Network—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
Tumor Location-weighted MRI-Report Contrastive Learning: A Framework for Improving the Explainability of Pediatric Brain Tumor Diagnosis—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
Artificial Intelligence Solution for Effective Treatment Planning for Glioblastoma Patients—0
Tumor segmentation on whole slide images: training or prompting?—0
PCA for Enhanced Cross-Dataset Generalizability in Breast Ultrasound Tumor Segmentation—0
PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training—0
A Review on End-To-End Methods for Brain Tumor Segmentation and Overall Survival Prediction—0
PEMMA: Parameter-Efficient Multi-Modal Adaptation for Medical Image Segmentation—0
A Review on Automated Brain Tumor Detection and Segmentation from MRI of Brain—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
TuNet: End-to-end Hierarchical Brain Tumor Segmentation using Cascaded Networks—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
A Prior Knowledge Based Tumor and Tumoral Subregion Segmentation Tool for Pediatric Brain Tumors—0
PriorNet: lesion segmentation in PET-CT including prior tumor appearance information—0
A Pretrained DenseNet Encoder for Brain Tumor Segmentation—0
Two-Stage Approach for Brain MR Image Synthesis: 2D Image Synthesis and 3D Refinement—0
Two-stage MR Image Segmentation Method for Brain Tumors based on Attention Mechanism—0
propnet: Propagating 2D Annotation to 3D Segmentation for Gastric Tumors on CT Scans—0
A Performance-Consistent and Computation-Efficient CNN System for High-Quality Automated Brain Tumor Segmentation—0
A Novel SLCA-UNet Architecture for Automatic MRI Brain Tumor Segmentation—0
A Novel Method for Automatic Segmentation of Brain Tumors in MRI Images—0
PSO-UNet: Particle Swarm-Optimized U-Net Framework for Precise Multimodal Brain Tumor Segmentation—0
Two Stage Segmentation of Cervical Tumors using PocketNet—0
Quantitative Impact of Label Noise on the Quality of Segmentation of Brain Tumors on MRI scans—0
QuantU-Net: Efficient Wearable Medical Imaging Using Bitwidth as a Trainable Parameter—0
3D PETCT Tumor Lesion Segmentation via GCN Refinement—0
QuickTumorNet: Fast Automatic Multi-Class Segmentation of Brain Tumors—0
Qutrit-inspired Fully Self-supervised Shallow Quantum Learning Network for Brain Tumor Segmentation—0
Radiomics as a measure superior to the Dice similarity coefficient for tumor segmentation performance evaluation—0
Generative Adversarial Networks for Weakly Supervised Generation and Evaluation of Brain Tumor Segmentations on MR Images—0
Anisotropic Hybrid Networks for liver tumor segmentation with uncertainty quantification—0
Uncertainty-driven refinement of tumor-core segmentation using 3D-to-2D networks with label uncertainty—0
RCA-IUnet: A residual cross-spatial attention guided inception U-Net model for tumor segmentation in breast ultrasound imaging—0
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