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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 551–575 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
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