@inproceedings{179726c75a3242579def48454294e260,
title = "Deep learning based Nucleus Classification in pancreas histological images",
abstract = "Tumor specimens contain a variety of healthy cells as well as cancerous cells, and this heterogeneity underlies resistance to various cancer therapies. But this problem has not been thoroughly investigated until recently. Meanwhile, technological breakthroughs in imaging have led to an explosion of molecular and cellular profiling data from large numbers of samples, and modern machine learning approaches including deep learning have been shown to produce encouraging results by finding hidden structures and make accurate predictions. In this paper, we propose a Deep learning based Nucleus Classification (DeepNC) approach using paired histopathology and immunofluorescence images (for label), and demonstrate its classification prediction power. This method can solve current issue on discrepancy between genomic- or transcriptomic-based and pathology-based tumor purity estimates by improving histological evaluation. We also explain challenges in training a deep learning model for huge dataset.",
keywords = "Deep Learning, Histopathology, Immunofluorescence, Segmentation",
author = "Chang, {Young Hwan} and Guillaume Thibault and Owen Madin and Vahid Azimi and Cole Meyers and Brett Johnson and Jason Link and Adam Margolin and Gray, {Joe W.}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 ; Conference date: 11-07-2017 Through 15-07-2017",
year = "2017",
month = sep,
day = "13",
doi = "10.1109/EMBC.2017.8036914",
language = "English",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "672--675",
booktitle = "2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society",
}