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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 351–400 of 786 papers

TitleStatusHype
PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image AnalysisCode1
Scratch Each Other's Back: Incomplete Multi-Modal Brain Tumor Segmentation via Category Aware Group Self-Support LearningCode1
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
Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive LearningCode1
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
Learning from partially labeled data for multi-organ and tumor segmentationCode1
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
Radiomics-enhanced Deep Multi-task Learning for Outcome Prediction in Head and Neck CancerCode1
ESKNet-An enhanced adaptive selection kernel convolution for breast tumors segmentationCode1
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
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic ModelCode3
Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation—0
Hyper-Connected Transformer Network for Multi-Modality PET-CT Segmentation—0
Synthetic Tumors Make AI Segment Tumors BetterCode2
CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation NetworkCode1
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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