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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 491–500 of 786 papers

TitleStatusHype
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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