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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 226–250 of 786 papers

TitleStatusHype
Unified HT-CNNs Architecture: Transfer Learning for Segmenting Diverse Brain Tumors in MRI from Gliomas to Pediatric Tumors—0
QCResUNet: Joint Subject-level and Voxel-level Segmentation Quality PredictionCode0
BATseg: Boundary-aware Multiclass Spinal Cord Tumor Segmentation on 3D MRI Scans—0
A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor SegmentationCode0
Magnetic Resonance Imaging Feature-Based Subtyping and Model Ensemble for Enhanced Brain Tumor SegmentationCode0
Low-Contrast-Enhanced Contrastive Learning for Semi-Supervised Endoscopic Image SegmentationCode0
Leveraging Semantic Asymmetry for Precise Gross Tumor Volume Segmentation of Nasopharyngeal Carcinoma in Planning CT—0
An Ensemble Approach for Brain Tumor Segmentation and Synthesis—0
Comparative Analysis of nnUNet and MedNeXt for Head and Neck Tumor Segmentation in MRI-guided RadiotherapyCode0
Automatic brain tumor segmentation in 2D intra-operative ultrasound images using MRI tumor annotationsCode0
Multimodal 3D Brain Tumor Segmentation with Adversarial Training and Conditional Random Field—0
Intensity-Spatial Dual Masked Autoencoder for Multi-Scale Feature Learning in Chest CT SegmentationCode0
TexLiverNet: Leveraging Medical Knowledge and Spatial-Frequency Perception for Enhanced Liver Tumor SegmentationCode0
MBDRes-U-Net: Multi-Scale Lightweight Brain Tumor Segmentation NetworkCode0
FedPID: An Aggregation Method for Federated Learning—0
A New Logic For Pediatric Brain Tumor SegmentationCode0
Tumor Location-weighted MRI-Report Contrastive Learning: A Framework for Improving the Explainability of Pediatric Brain Tumor Diagnosis—0
Lung tumor segmentation in MRI mice scans using 3D nnU-Net with minimum annotations—0
Enhancing Brain Tumor Classification Using TrAdaBoost and Multi-Classifier Deep Learning Approaches—0
AEPL: Automated and Editable Prompt Learning for Brain Tumor Segmentation—0
Multi-Layer Feature Fusion with Cross-Channel Attention-Based U-Net for Kidney Tumor Segmentation—0
Segmentation of Pediatric Brain Tumors using a Radiologically informed, Deep Learning Cascade—0
Gradient Map-Assisted Head and Neck Tumor Segmentation: A Pre-RT to Mid-RT Approach in MRI-Guided RadiotherapyCode0
Two-Stage Approach for Brain MR Image Synthesis: 2D Image Synthesis and 3D Refinement—0
SmoothSegNet: A Global-Local Framework for Liver Tumor Segmentation with Clinical KnowledgeInformed Label SmoothingCode0
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