Stopping Rules for Majority Voting: A Public Choice Experiment

2018 ◽  
Author(s):  
Keith L. Dougherty ◽  
Alice Kisaalita ◽  
Jordan McKissick ◽  
Evan Katz
Keyword(s):  
Public Choice ◽  
Choice Experiment ◽  
Majority Voting ◽  
Stopping Rules

Stopping rules for majority voting: A public choice experiment

2020 ◽  
Vol 175 ◽  
pp. 353-364 ◽  
Author(s):  
Keith L. Dougherty ◽  
Alice Kisaalita ◽  
Jordan McKissick ◽  
Evan Katz
Keyword(s):  
Public Choice ◽  
Choice Experiment ◽  
Majority Voting ◽  
Stopping Rules

The essential ingredients of research supervision: A discrete-choice experiment.

2019 ◽  
Vol 111 (7) ◽  
pp. 1243-1260 ◽  
Author(s):  
Alex Roach ◽  
Bruce K. Christensen ◽  
Elizabeth Rieger
Keyword(s):  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Choice Experiment ◽  
Research Supervision

INDIVIDUALS' PREFERENCES FOR ESOPHAGEAL CANCER SCREENING STRATEGIES: A DISCRETE CHOICE EXPERIMENT

2019 ◽  
Author(s):  
Y Peters ◽  
E van Grinsven ◽  
M van de Haterd ◽  
D van Lankveld ◽  
J Verbakel ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Cancer Screening ◽  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Choice Experiment ◽  
Screening Strategies

Forest Carbon Supply In Nepal: Evidence From A Choice Experiment

2018 ◽  
Author(s):  
Sahan Dissanayake ◽  
Randall A. Bluffstone ◽  
E. Somanathan ◽  
Harisharan Luintel ◽  
N. S. Paudel ◽  
...  
Keyword(s):  
Choice Experiment ◽  
Forest Carbon ◽  
Carbon Supply

Pathology image-based lung cancer subtyping using deeplearning features and cell-density maps

2020 ◽  
Vol 2020 (10) ◽  
pp. 64-1-64-5
Author(s):  
Mustafa I. Jaber ◽  
Christopher W. Szeto ◽  
Bing Song ◽  
Liudmila Beziaeva ◽  
Stephen C. Benz ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cell Density ◽  
Majority Voting ◽  
Training Set ◽  
Density Maps ◽  
Color Deconvolution ◽  
Map Generation ◽  
Density Map ◽  
Pathology Image ◽  
Whole Slide Images

In this paper, we propose a patch-based system to classify non-small cell lung cancer (NSCLC) diagnostic whole slide images (WSIs) into two major histopathological subtypes: adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC). Classifying patients accurately is important for prognosis and therapy decisions. The proposed system was trained and tested on 876 subtyped NSCLC gigapixel-resolution diagnostic WSIs from 805 patients – 664 in the training set and 141 in the test set. The algorithm has modules for: 1) auto-generated tumor/non-tumor masking using a trained residual neural network (ResNet34), 2) cell-density map generation (based on color deconvolution, local drain segmentation, and watershed transformation), 3) patch-level feature extraction using a pre-trained ResNet34, 4) a tower of linear SVMs for different cell ranges, and 5) a majority voting module for aggregating subtype predictions in unseen testing WSIs. The proposed system was trained and tested on several WSI magnifications ranging from x4 to x40 with a best ROC AUC of 0.95 and an accuracy of 0.86 in test samples. This fully-automated histopathology subtyping method outperforms similar published state-of-the-art methods for diagnostic WSIs.

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