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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 751–775 of 786 papers

TitleStatusHype
Deep LOGISMOS: Deep Learning Graph-based 3D Segmentation of Pancreatic Tumors on CT scans—0
MRI Tumor Segmentation with Densely Connected 3D CNNCode0
BUSIS: A Benchmark for Breast Ultrasound Image SegmentationCode1
2D-Densely Connected Convolution Neural Networks for automatic Liver and Tumor Segmentation—0
Brain Tumor Segmentation Based on Refined Fully Convolutional Neural Networks with A Hierarchical Dice LossCode0
Partial Labeled Gastric Tumor Segmentation via patch-based Reiterative Learning—0
3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic VolumesCode0
Segmenting Brain Tumors with Symmetry—0
Automated Tumor Segmentation and Brain Mapping for the Tumor Area—0
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
3D Convolutional Neural Networks for Brain Tumor Segmentation: A Comparison of Multi-resolution Architectures—0
Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks—0
Multimodal MRI brain tumor segmentation using random forests with features learned from fully convolutional neural network—0
Automatic Liver Lesion Segmentation Using A Deep Convolutional Neural Network Method—0
Automatic Liver Lesion Detection using Cascaded Deep Residual Networks—0
SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D convolutional neural networksCode0
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