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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 451–475 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
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