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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 601–650 of 786 papers

TitleStatusHype
Targeted Neural Architectures in Multi-Objective Frameworks for Complete Glioma Characterization from Multimodal MRI—0
Recommender Engine Driven Client Selection in Federated Brain Tumor Segmentation—0
Uncertainty-Guided Coarse-to-Fine Tumor Segmentation with Anatomy-Aware Post-Processing—0
Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation—0
Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIs—0
Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentation—0
Region of Interest Identification for Brain Tumors in Magnetic Resonance Images—0
3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraint—0
Relevance analysis of MRI sequences for automatic liver tumor segmentation—0
Rel-UNet: Reliable Tumor Segmentation via Uncertainty Quantification in nnU-Net—0
An Inductive Transfer Learning Approach using Cycle-consistent Adversarial Domain Adaptation with Application to Brain Tumor Segmentation—0
Response monitoring of breast cancer on DCE-MRI using convolutional neural network-generated seed points and constrained volume growing—0
Whole-body tumor segmentation of 18F -FDG PET/CT using a cascaded and ensembled convolutional neural networks—0
An Exceptional Dataset For Rare Pancreatic Tumor Segmentation—0
RobU-Net: a heuristic robust multi-class brain tumor segmentation approaches for MRI scans—0
Robust Automatic Whole Brain Extraction on Magnetic Resonance Imaging of Brain Tumor Patients using Dense-Vnet—0
Robustifying deep networks for image segmentation—0
Robust Learning Protocol for Federated Tumor Segmentation Challenge—0
A Bayesian approach to tissue-fraction estimation for oncological PET segmentation—0
Robustness of Brain Tumor Segmentation—0
Robust Pancreatic Ductal Adenocarcinoma Segmentation with Multi-Institutional Multi-Phase Partially-Annotated CT Scans—0
An Ensemble Approach for Brain Tumor Segmentation and Synthesis—0
Robust Tumor Segmentation with Hyperspectral Imaging and Graph Neural Networks—0
Unified HT-CNNs Architecture: Transfer Learning for Segmenting Diverse Brain Tumors in MRI from Gliomas to Pediatric Tumors—0
An End-to-End learnable Flow Regularized Model for Brain Tumor Segmentation—0
Scribble-based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation—0
United adversarial learning for liver tumor segmentation and detection of multi-modality non-contrast MRI—0
Seeing Beyond Cancer: Multi-Institutional Validation of Object Localization and 3D Semantic Segmentation using Deep Learning for Breast MRI—0
SAR: Scale-Aware Restoration Learning for 3D Tumor Segmentation—0
Segment Anything Model for Brain Tumor Segmentation—0
Unpaired cross-modality educed distillation (CMEDL) for medical image segmentation—0
Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging—0
Segmentation and Risk Score Prediction of Head and Neck Cancers in PET/CT Volumes with 3D U-Net and Cox Proportional Hazard Neural Networks—0
Segmentation of brain tumor on magnetic resonance imaging using a convolutional architecture—0
Segmentation of Glioma Tumors in Brain Using Deep Convolutional Neural Network—0
Segmentation of Kidney Tumors on Non-Contrast CT Images using Protuberance Detection Network—0
Segmentation of Liver Lesions with Reduced Complexity Deep Models—0
Segmentation of Lung Tumor from CT Images using Deep Supervision—0
Segmentation of Pediatric Brain Tumors using a Radiologically informed, Deep Learning Cascade—0
Segmenting Brain Tumors with Symmetry—0
Unsupervised Brain Tumor Segmentation with Image-based Prompts—0
Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging—0
3D Kidneys and Kidney Tumor Semantic Segmentation using Boundary-Aware Networks—0
Self-calibrated convolution towards glioma segmentation—0
An Efficient Solution for Breast Tumor Segmentation and Classification in Ultrasound Images Using Deep Adversarial Learning—0
Self-semantic contour adaptation for cross modality brain tumor segmentation—0
Self-supervised Feature Learning for 3D Medical Images by Playing a Rubik's Cube—0
Self-Supervised Learning for 3D Medical Image Analysis using 3D SimCLR and Monte Carlo Dropout—0
Self-Supervised Learning for Organs At Risk and Tumor Segmentation with Uncertainty Quantification—0
Self-supervised learning improves robustness of deep learning lung tumor segmentation to CT imaging differences—0
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