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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 476–500 of 786 papers

TitleStatusHype
Segmentation of Glioma Tumors in Brain Using Deep Convolutional Neural Network—0
Segmentation of Kidney Tumors on Non-Contrast CT Images using Protuberance Detection Network—0
Segmentation of Liver Lesions with Reduced Complexity Deep Models—0
Segmentation of Lung Tumor from CT Images using Deep Supervision—0
Segmentation of Pediatric Brain Tumors using a Radiologically informed, Deep Learning Cascade—0
Segmenting Brain Tumors with Symmetry—0
Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging—0
Self-calibrated convolution towards glioma segmentation—0
Self-semantic contour adaptation for cross modality brain tumor segmentation—0
Self-supervised Feature Learning for 3D Medical Images by Playing a Rubik's Cube—0
Self-Supervised Learning for 3D Medical Image Analysis using 3D SimCLR and Monte Carlo Dropout—0
Self-Supervised Learning for Organs At Risk and Tumor Segmentation with Uncertainty Quantification—0
Self-supervised learning improves robustness of deep learning lung tumor segmentation to CT imaging differences—0
Self-supervised Tumor Segmentation through Layer Decomposition—0
Semantic Feature Attention Network for Liver Tumor Segmentation in Large-scale CT database—0
Sequential 3D U-Nets for Biologically-Informed Brain Tumor Segmentation—0
Simulation of Arbitrary Level Contrast Dose in MRI Using an Iterative Global Transformer Model—0
Slice-by-slice deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for spatial uncertainty on FDG PET and CT images—0
Slice Imputation: Intermediate Slice Interpolation for Anisotropic 3D Medical Image Segmentation—0
SLPT: Selective Labeling Meets Prompt Tuning on Label-Limited Lesion Segmentation—0
SoftSeg: Advantages of soft versus binary training for image segmentation—0
Soft Tissue Sarcoma Co-Segmentation in Combined MRI and PET/CT Data—0
Source Identification: A Self-Supervision Task for Dense Prediction—0
Spatially Covariant Lesion Segmentation—0
Spectral U-Net: Enhancing Medical Image Segmentation via Spectral Decomposition—0
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