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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 371–380 of 786 papers

TitleStatusHype
Cheap Lunch for Medical Image Segmentation by Fine-tuning SAM on Few Exemplars—0
CBCTLiTS: A Synthetic, Paired CBCT/CT Dataset For Segmentation And Style Transfer—0
A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction—0
A Hybrid Framework for Tumor Saliency Estimation—0
CASPIANET++: A Multidimensional Channel-Spatial Asymmetric Attention Network with Noisy Student Curriculum Learning Paradigm for Brain Tumor Segmentation—0
Cascaded Volumetric Convolutional Network for Kidney Tumor Segmentation from CT volumes—0
Cascaded V-Net using ROI masks for brain tumor segmentation—0
CARE: A Large Scale CT Image Dataset and Clinical Applicable Benchmark Model for Rectal Cancer Segmentation—0
Can Foundation Models Really Segment Tumors? A Benchmarking Odyssey in Lung CT Imaging—0
A Structural Graph-Based Method for MRI Analysis—0
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