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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 611–620 of 786 papers

TitleStatusHype
Modality-Pairing Learning for Brain Tumor Segmentation—0
SAR: Scale-Aware Restoration Learning for 3D Tumor Segmentation—0
Selective Information Passing for MR/CT Image SegmentationCode0
ESTAN: Enhanced Small Tumor-Aware Network for Breast Ultrasound Image Segmentation—0
Enhancing MRI Brain Tumor Segmentation with an Additional Classification Network—0
Brain Tumor Segmentation using 3D-CNNs with Uncertainty Estimation—0
Radiologist-level Performance by Using Deep Learning for Segmentation of Breast Cancers on MRI Scans—0
Qutrit-inspired Fully Self-supervised Shallow Quantum Learning Network for Brain Tumor Segmentation—0
Multi-channel MRI Embedding: An EffectiveStrategy for Enhancement of Human Brain WholeTumor SegmentationCode0
Brain Tumor Survival Prediction using Radiomics Features—0
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