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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 451–500 of 786 papers

TitleStatusHype
Investigation of Network Architecture for Multimodal Head-and-Neck Tumor Segmentation—0
Multimodal CNN Networks for Brain Tumor Segmentation in MRI: A BraTS 2022 Challenge Solution—0
Robust Learning Protocol for Federated Tumor Segmentation Challenge—0
Towards fully automated deep-learning-based brain tumor segmentation: is brain extraction still necessary?Code0
M-GenSeg: Domain Adaptation For Target Modality Tumor Segmentation With Annotation-Efficient SupervisionCode0
Investigating certain choices of CNN configurations for brain lesion segmentation—0
Mind the Gap: Scanner-induced domain shifts pose challenges for representation learning in histopathology—0
DIGEST: Deeply supervIsed knowledGE tranSfer neTwork learning for brain tumor segmentation with incomplete multi-modal MRI scans—0
Encoding feature supervised UNet++: Redesigning Supervision for liver and tumor segmentation—0
Improved HER2 Tumor Segmentation with Subtype Balancing using Deep Generative Networks—0
Generative Adversarial Networks for Weakly Supervised Generation and Evaluation of Brain Tumor Segmentations on MR Images—0
ISA-Net: Improved spatial attention network for PET-CT tumor segmentation—0
Using U-Net Network for Efficient Brain Tumor Segmentation in MRI Images—0
Exploring Structure-Wise Uncertainty for 3D Medical Image Segmentation—0
Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation—0
Hyper-Connected Transformer Network for Multi-Modality PET-CT Segmentation—0
Brain Tumor Segmentation using Enhanced U-Net Model with Empirical AnalysisCode0
Improved automated lesion segmentation in whole-body FDG/PET-CT via Test-Time AugmentationCode0
Whole-body tumor segmentation of 18F -FDG PET/CT using a cascaded and ensembled convolutional neural networks—0
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
Recurrence-free Survival Prediction under the Guidance of Automatic Gross Tumor Volume Segmentation for Head and Neck CancersCode0
Automated head and neck tumor segmentation from 3D PET/CT—0
Deep Superpixel Generation and Clustering for Weakly Supervised Segmentation of Brain Tumors in MR Images—0
Memory Consistent Unsupervised Off-the-Shelf Model Adaptation for Source-Relaxed Medical Image Segmentation—0
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
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
Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation—0
Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentation—0
Analyzing Deep Learning Based Brain Tumor Segmentation with Missing MRI Modalities—0
Deep Learning and Health Informatics for Smart Monitoring and Diagnosis—0
Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Image Segmentation—0
A Transformer-based Generative Adversarial Network for Brain Tumor Segmentation—0
PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training—0
Adaptive Active Contour Model for Brain Tumor SegmentationCode0
Large-Kernel Attention for 3D Medical Image Segmentation—0
Brain MRI study for glioma segmentation using convolutional neural networks and original post-processing techniques with low computational demand—0
CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer with Modality-Correlated Cross-Attention for Brain Tumor Segmentation—0
Slice-by-slice deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for spatial uncertainty on FDG PET and CT images—0
Free-form Lesion Synthesis Using a Partial Convolution Generative Adversarial Network for Enhanced Deep Learning Liver Tumor Segmentation—0
Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation—0
Parotid Gland MRI Segmentation Based on Swin-Unet and Multimodal Images—0
CTVR-EHO TDA-IPH Topological Optimized Convolutional Visual Recurrent Network for Brain Tumor Segmentation and Classification—0
Decoupled Pyramid Correlation Network for Liver Tumor Segmentation from CT images—0
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