Using Ensemble Models in the Histological Examination of Tissue Abnormalities
Computer Vision and Pattern Recognition
2015-05-18 v1 Computational Engineering, Finance, and Science
Machine Learning
Abstract
Classification models for the automatic detection of abnormalities on histological samples do exists, with an active debate on the cost associated with false negative diagnosis (underdiagnosis) and false positive diagnosis (overdiagnosis). Current models tend to underdiagnose, failing to recognize a potentially fatal disease. The objective of this study is to investigate the possibility of automatically identifying abnormalities in tissue samples through the use of an ensemble model on data generated by histological examination and to minimize the number of false negative cases.
Cite
@article{arxiv.1505.03932,
title = {Using Ensemble Models in the Histological Examination of Tissue Abnormalities},
author = {Giancarlo Crocetti and Michael Coakley and Phil Dressner and Wanda Kellum and Tamba Lamin},
journal= {arXiv preprint arXiv:1505.03932},
year = {2015}
}
Comments
4 pages, 4 tables, 3 figures. Proceedings of 12th Annual Research Day, 2014 - Pace University