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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 191–200 of 786 papers

TitleStatusHype
DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images—0
A Hybrid Framework for Tumor Saliency Estimation—0
Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging—0
CU-Net: a U-Net architecture for efficient brain-tumor segmentation on BraTS 2019 dataset—0
A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction—0
3D Kidneys and Kidney Tumor Semantic Segmentation using Boundary-Aware Networks—0
CU-Net: Cascaded U-Net with Loss Weighted Sampling for Brain Tumor Segmentation—0
A Structural Graph-Based Method for MRI Analysis—0
Cross-Organ and Cross-Scanner Adenocarcinoma Segmentation using Rein to Fine-tune Vision Foundation Models—0
Cross-modality (CT-MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets—0
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