Circulating Tumor Enumeration using Deep Learning

Stephen Obonyo, Joseph Orero

Abstract

Cancer is the third most killer disease just after infectious and cardiovascular diseases. Existing cancer treatment methods vary among patients based on the type and stage of tumor development. Treatment modalities such as chemotherapy, surgery and radiation are successful when the disease is detected early and regularly monitored. Enumeration and detection of Circulating Tumor Cells (CTC’s) is a key monitoring method which involves identification of cancer related substances known as tumor markers which are excreted by primary tumors into patient’s blood. The presence, absence or number of CTC’s in blood can be used as treatment metric indicator. As such, the metric can be used to evaluate patient’s disease progression and determine effectiveness of a treatment option a patient is subjected to. In this paper, we present a deep learning model based on Convolutional Neural Network which learns and enumerates CTC’s from stained image samples. With no human intervention, the model learns the best set of representations to enumerate CTC’s.

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Paper Citation


in Harvard Style

Obonyo S. and Orero J. (2018). Circulating Tumor Enumeration using Deep Learning.In Proceedings of the 10th International Joint Conference on Computational Intelligence - Volume 1: IJCCI, ISBN 978-989-758-327-8, pages 297-303. DOI: 10.5220/0007232602970303


in Bibtex Style

@conference{ijcci18,
author={Stephen Obonyo and Joseph Orero},
title={Circulating Tumor Enumeration using Deep Learning},
booktitle={Proceedings of the 10th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,},
year={2018},
pages={297-303},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007232602970303},
isbn={978-989-758-327-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,
TI - Circulating Tumor Enumeration using Deep Learning
SN - 978-989-758-327-8
AU - Obonyo S.
AU - Orero J.
PY - 2018
SP - 297
EP - 303
DO - 10.5220/0007232602970303