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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 551–600 of 786 papers

TitleStatusHype
RFNet: Region-Aware Fusion Network for Incomplete Multi-Modal Brain Tumor SegmentationCode1
H2NF-Net for Brain Tumor Segmentation using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task—0
MRI brain tumor segmentation and uncertainty estimation using 3D-UNet architecturesCode1
Combining CNN and Hybrid Active Contours for Head and Neck Tumor Segmentation in CT and PET images—0
Multiclass Spinal Cord Tumor Segmentation on MRI with Deep Learning—0
QuickTumorNet: Fast Automatic Multi-Class Segmentation of Brain Tumors—0
A Feasibility study for Deep learning based automated brain tumor segmentation using Magnetic Resonance Images—0
A Multi-View Dynamic Fusion Framework: How to Improve the Multimodal Brain Tumor Segmentation from Multi-Views?—0
HI-Net: Hyperdense Inception 3D UNet for Brain Tumor Segmentation—0
Uncertainty-driven refinement of tumor-core segmentation using 3D-to-2D networks with label uncertainty—0
Annotation-efficient deep learning for automatic medical image segmentationCode1
Esophageal Tumor Segmentation in CT Images using Dilated Dense Attention Unet (DDAUnet)Code1
A Multi-task Contextual Atrous Residual Network for Brain Tumor Detection & Segmentation—0
Inter-slice Context Residual Learning for 3D Medical Image SegmentationCode1
Efficient embedding network for 3D brain tumor segmentation—0
DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasetsCode1
SoftSeg: Advantages of soft versus binary training for image segmentation—0
Multi-Decoder Networks with Multi-Denoising Inputs for Tumor Segmentation—0
Automatic Brain Tumor Segmentation with Scale Attention NetworkCode0
Covariance Self-Attention Dual Path UNet for Rectal Tumor Segmentation—0
DR-Unet104 for Multimodal MRI brain tumor segmentationCode1
A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor SegmentationCode1
Generalized Wasserstein Dice Score, Distributionally Robust Deep Learning, and Ranger for brain tumor segmentation: BraTS 2020 challengeCode1
nnU-Net for Brain Tumor SegmentationCode1
Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solutionCode1
Brain Tumor Segmentation Network Using Attention-based Fusion and Spatial Relationship Constraint—0
Impact of Spherical Coordinates Transformation Pre-processing in Deep Convolution Neural Networks for Brain Tumor Segmentation and Survival Prediction—0
Does anatomical contextual information improve 3D U-Net based brain tumor segmentation?—0
What is the best data augmentation for 3D brain tumor segmentation?Code1
Context Aware 3D UNet for Brain Tumor Segmentation—0
ivadomed: A Medical Imaging Deep Learning ToolboxCode1
Modality-Pairing Learning for Brain Tumor Segmentation—0
SAR: Scale-Aware Restoration Learning for 3D Tumor Segmentation—0
Selective Information Passing for MR/CT Image SegmentationCode0
ESTAN: Enhanced Small Tumor-Aware Network for Breast Ultrasound Image Segmentation—0
Enhancing MRI Brain Tumor Segmentation with an Additional Classification Network—0
Brain Tumor Segmentation using 3D-CNNs with Uncertainty Estimation—0
MA-Net: A Multi-Scale Attention Network for Liver and Tumor SegmentationCode3
Radiologist-level Performance by Using Deep Learning for Segmentation of Breast Cancers on MRI Scans—0
Qutrit-inspired Fully Self-supervised Shallow Quantum Learning Network for Brain Tumor Segmentation—0
Multi-channel MRI Embedding: An EffectiveStrategy for Enhancement of Human Brain WholeTumor SegmentationCode0
Brain Tumor Survival Prediction using Radiomics Features—0
Soft Tissue Sarcoma Co-Segmentation in Combined MRI and PET/CT Data—0
A Cascaded Deep-Learning Framework for Segmentation of Metastatic Brain Tumors Before and After Stereotactic Radiation Therapy—0
Robust Pancreatic Ductal Adenocarcinoma Segmentation with Multi-Institutional Multi-Phase Partially-Annotated CT Scans—0
Spherical coordinates transformation pre-processing in Deep Convolution Neural Networks for brain tumor segmentation in MRI—0
Abstracting Deep Neural Networks into Concept Graphs for Concept Level InterpretabilityCode1
Multimodal Spatial Attention Module for Targeting Multimodal PET-CT Lung Tumor Segmentation—0
A Computation-Efficient CNN System for High-Quality Brain Tumor Segmentation—0
Adding Seemingly Uninformative Labels Helps in Low Data RegimesCode1
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