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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 551–560 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
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