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Found 3 papers, 2 papers with code
Crowd-Certain: Label Aggregation in Crowdsourced and Ensemble Learning Classification
In this paper, we introduce Crowd-Certain, a novel approach for label aggregation in crowdsourced and ensemble learning classification tasks that offers improved performance and computational efficiency for different numbers of annotators and a variety of datasets.
Deep learning classification of chest x-ray images
We applied our method to the classification of two example pathologies, pulmonary nodules and cardiomegaly, and we compared the performance of our method to three existing methods.
Automated Thalamic Nuclei Segmentation Using Multi-Planar Cascaded Convolutional Neural Networks
For 7T WMn-MPRAGE, the proposed method outperforms current state-of-the-art on patients with ET with statistically significant improvements in Dice for five nuclei (increase in the range of 0. 05-0. 18) and VSI for four nuclei (increase in the range of 0. 05-0. 19), while performing comparably for healthy and MS subjects.
