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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 341–350 of 786 papers

TitleStatusHype
Enhancing Brain Tumor Classification Using TrAdaBoost and Multi-Classifier Deep Learning Approaches—0
End-to-End Cascaded U-Nets with a Localization Network for Kidney Tumor Segmentation—0
Global Planar Convolutions for improved context aggregation in Brain Tumor Segmentation—0
Feature-enhanced Generation and Multi-modality Fusion based Deep Neural Network for Brain Tumor Segmentation with Missing MR Modalities—0
Feature Fusion Encoder Decoder Network For Automatic Liver Lesion Segmentation—0
A Novel Method for Automatic Segmentation of Brain Tumors in MRI Images—0
FedCostWAvg: A new averaging for better Federated Learning—0
Federated brain tumor segmentation: an extensive benchmark—0
HANS-Net: Hyperbolic Convolution and Adaptive Temporal Attention for Accurate and Generalizable Liver and Tumor Segmentation in CT Imaging—0
End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks—0
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