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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 251–260 of 786 papers

TitleStatusHype
Decentralized Differentially Private Segmentation with PATE—0
Decentralized Gossip Mutual Learning (GML) for automatic head and neck tumor segmentation—0
Decentralized Gossip Mutual Learning (GML) for brain tumor segmentation on multi-parametric MRI—0
Decoupled Pyramid Correlation Network for Liver Tumor Segmentation from CT images—0
Efficient Parameter Adaptation for Multi-Modal Medical Image Segmentation and Prognosis—0
Enhancing MRI Brain Tumor Segmentation with an Additional Classification Network—0
ESTAN: Enhanced Small Tumor-Aware Network for Breast Ultrasound Image Segmentation—0
A Review on End-To-End Methods for Brain Tumor Segmentation and Overall Survival Prediction—0
Artificial Intelligence Solution for Effective Treatment Planning for Glioblastoma Patients—0
Disentangled Multimodal Brain MR Image Translation via Transformer-based Modality Infuser—0
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