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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 551–575 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
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