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Found 7 papers, 1 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.
Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network
In this paper, we propose a simple machine learning approach called the graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E).
Robust Segmentation of Cell Nuclei in 3-D Microscopy Images
Like other segmentation algorithms, we first use a seed detection/marker extraction algorithm to find a seed voxel for each individual cell nucleus.
Object sieving and morphological closing to reduce false detections in wide-area aerial imagery
We use two wide-area aerial videos to compare the performance of five object detection algorithms in the absence and in the presence of our post-processing scheme.
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.
A Conditional Random Field Model for Context Aware Cloud Detection in Sky Images
A conditional random field (CRF) model for cloud detection in ground based sky images is presented.
