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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 376–400 of 786 papers

TitleStatusHype
Brain Tumor Segmentation using Enhanced U-Net Model with Empirical AnalysisCode0
Whole-body tumor segmentation of 18F -FDG PET/CT using a cascaded and ensembled convolutional neural networks—0
Improved automated lesion segmentation in whole-body FDG/PET-CT via Test-Time AugmentationCode0
Exploring Vanilla U-Net for Lesion Segmentation from Whole-body FDG-PET/CT ScansCode1
Improving Deep Learning Models for Pediatric Low-Grade Glioma Tumors Molecular Subtype Identification Using 3D Probability Distributions of Tumor Location—0
MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network—0
Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-oncology (I3CR-WANO)Code0
FedGraph: an Aggregation Method from Graph Perspective—0
PriorNet: lesion segmentation in PET-CT including prior tumor appearance information—0
An Anatomy-aware Framework for Automatic Segmentation of Parotid Tumor from Multimodal MRI—0
Automated head and neck tumor segmentation from 3D PET/CT—0
Recurrence-free Survival Prediction under the Guidance of Automatic Gross Tumor Volume Segmentation for Head and Neck CancersCode0
Deep Superpixel Generation and Clustering for Weakly Supervised Segmentation of Brain Tumors in MR Images—0
Hybrid Window Attention Based Transformer Architecture for Brain Tumor SegmentationCode1
Automatic Tumor Segmentation via False Positive Reduction Network for Whole-Body Multi-Modal PET/CT ImagesCode1
Memory Consistent Unsupervised Off-the-Shelf Model Adaptation for Source-Relaxed Medical Image Segmentation—0
Rethinking the Unpretentious U-net for Medical Ultrasound Image SegmentationCode1
TMSS: An End-to-End Transformer-based Multimodal Network for Segmentation and Survival PredictionCode1
AutoPET Challenge: Combining nn-Unet with Swin UNETR Augmented by Maximum Intensity Projection ClassifierCode0
AutoPET Challenge 2022: Automatic Segmentation of Whole-body Tumor Lesion Based on Deep Learning and FDG PET/CTCode0
NestedFormer: Nested Modality-Aware Transformer for Brain Tumor SegmentationCode1
Learning Multi-Modal Brain Tumor Segmentation from Privileged Semi-Paired MRI Images with Curriculum Disentanglement Learning—0
Segmentation of Parotid Gland Tumors Using Multimodal MRI and Contrastive Learning—0
SFusion: Self-attention based N-to-One Multimodal Fusion BlockCode1
Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation—0
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