Partially Labeled Datasets
5 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Partially Labeled Datasets
Most implemented papers
Multi-organ Segmentation over Partially Labeled Datasets with Multi-scale Feature Abstraction
Shortage of fully annotated datasets has been a limiting factor in developing deep learning based image segmentation algorithms and the problem becomes more pronounced in multi-organ segmentation.
Learning from Multiple Datasets with Heterogeneous and Partial Labels for Universal Lesion Detection in CT
For example, DeepLesion is such a large-scale CT image dataset with lesions of various types, but it also has many unlabeled lesions (missing annotations).
DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasets
jianpengz/DoDNet
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CVPR 2021
To address this, we propose a dynamic on-demand network (DoDNet) that learns to segment multiple organs and tumors on partially labeled datasets.
Federated Multi-organ Segmentation with Inconsistent Labels
Extensive experiments on six public abdominal CT datasets show that our Fed-MENU method can effectively obtain a federated learning model using the partially labeled datasets with superior performance to other models trained by either localized or centralized learning methods.
Learning from partially labeled data for multi-organ and tumor segmentation
jianpengz/DoDNet
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To address this, we propose a Transformer based dynamic on-demand network (TransDoDNet) that learns to segment organs and tumors on multiple partially labeled datasets.
