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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 531–540 of 786 papers

TitleStatusHype
Transfer learning for automatic brain tumor classification Using MRI Images.—0
Transfer Learning for Brain Tumor Segmentation—0
Transfer Learning in Magnetic Resonance Brain Imaging: a Systematic Review—0
Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation—0
TransMed: Transformers Advance Multi-modal Medical Image Classification—0
Treatment-aware Diffusion Probabilistic Model for Longitudinal MRI Generation and Diffuse Glioma Growth Prediction—0
Quantifying uncertainty in lung cancer segmentation with foundation models applied to mixed-domain datasets—0
Trustworthy Multi-phase Liver Tumor Segmentation via Evidence-based Uncertainty—0
Tumor-Centered Patching for Enhanced Medical Image Segmentation—0
Improving Deep Learning Models for Pediatric Low-Grade Glioma Tumors Molecular Subtype Identification Using 3D Probability Distributions of Tumor Location—0
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