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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 761–770 of 786 papers

TitleStatusHype
Fast PET Scan Tumor Segmentation using Superpixels, Principal Component Analysis and K-means Clustering—0
Hierarchical Convolutional-Deconvolutional Neural Networks for Automatic Liver and Tumor Segmentation—0
A Multiscale Patch Based Convolutional Network for Brain Tumor Segmentation—0
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT VolumesCode0
Sequential 3D U-Nets for Biologically-Informed Brain Tumor Segmentation—0
Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural NetworksCode0
Segmentation of Glioma Tumors in Brain Using Deep Convolutional Neural Network—0
DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image SegmentationCode0
SegAN: Adversarial Network with Multi-scale L_1 Loss for Medical Image SegmentationCode0
Neural Network-Based Automatic Liver Tumor Segmentation With Random Forest-Based Candidate Filtering—0
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