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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 591–600 of 786 papers

TitleStatusHype
Brain Tumor Segmentation and Survival Prediction using Automatic Hard mining in 3D CNN Architecture—0
Multi-stage Deep Layer Aggregation for Brain Tumor Segmentation—0
H2NF-Net for Brain Tumor Segmentation using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task—0
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
A Feasibility study for Deep learning based automated brain tumor segmentation using Magnetic Resonance Images—0
QuickTumorNet: Fast Automatic Multi-Class Segmentation of Brain Tumors—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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