@inproceedings{5f4564aa984042a6b04a8ac05342a8c8,
title = "Automatic classification of cancer cells in multispectral microscopic images of lymph node samples",
abstract = "Histopathological analysis is crucial for the diagnosis of a large number of cancer types. A lot of progress has been made in the development of molecular based assays, but many of the cases still require the careful analysis of the stained tissue under a bright-field microscope and its analysis. This procedure is costly and time-consuming. We present a novel method for classification of cancer cells in lymph node images. It is based on the measurement of the spectral image of hematoxylin and eosin stained sample under the microscope and the analysis of the acquired data using state of the art machine learning techniques. The method is based on the analysis of the spectral information of the cells as well as their morphological properties. A large number of descriptors is extracted for each cell location, which are used to train a supervised classifier which discriminates between normal and cancer cells. We show that a reliable analysis can be made with detection rate (recall) of 81%-100% for the cancer class.",
author = "Gali Zimmerman-Moreno and Irina Marin and Moshe Lindner and Iris Barshack and Yuval Garini and Eli Konen",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 ; Conference date: 16-08-2016 Through 20-08-2016",
year = "2016",
month = oct,
day = "13",
doi = "10.1109/EMBC.2016.7591597",
language = "الإنجليزيّة",
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 = "3973--3976",
booktitle = "2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016",
address = "الولايات المتّحدة",
}
