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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 201–210 of 786 papers

TitleStatusHype
Cascaded Volumetric Convolutional Network for Kidney Tumor Segmentation from CT volumes—0
Dealing with All-stage Missing Modality: Towards A Universal Model with Robust Reconstruction and Personalization—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
DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images—0
A Structural Graph-Based Method for MRI Analysis—0
Can Foundation Models Really Segment Tumors? A Benchmarking Odyssey in Lung CT Imaging—0
CBCTLiTS: A Synthetic, Paired CBCT/CT Dataset For Segmentation And Style Transfer—0
Cheap Lunch for Medical Image Segmentation by Fine-tuning SAM on Few Exemplars—0
Decentralized Differentially Private Segmentation with PATE—0
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