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Cytopathological image analysis using deep-learning networks in microfluidic microscopy

Publication Type : Journal Article

Publisher : Journal of the Optical Society of America A

Source : Journal of the Optical Society of America A, Volume 34, Issue 1, p.111-121 (2017)

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Campus : Amritapuri

School : Department of Computer Science and Engineering, School of Engineering

Center : Computer Vision and Robotics, Research & Projects

Department : Computer Science

Verified : No

Year : 2017

Abstract : Cytopathologic testing is one of the most critical steps in the diagnosis of diseases, including cancer. However, the task is laborious and demands skill. Associated high cost and low throughput drew considerable interest in automating the testing process. Several neural network architectures were designed to provide human expertise to machines. In this paper, we explore and propose the feasibility of using deep-learning networks for cytopathologic analysis by performing the classification of three important unlabeled, unstained leukemia cell lines (K562, MOLT, and HL60). The cell images used in the classification are captured using a low-cost, high-throughput cell imaging technique: microfluidics-based imaging flow cytometry. We demonstrate that without any conventional fine segmentation followed by explicit feature extraction, the proposed deep-learning algorithms effectively classify the coarsely localized cell lines. We show that the designed deep belief network as well as the deeply pretrained convolutional neural network outperform the conventionally used decision systems and are important in the medical domain, where the availability of labeled data is limited for training. We hope that our work enables the development of a clinically significant high-throughput microfluidic microscopy-based tool for disease screening/triaging, especially in resource-limited settings.

Cite this Research Publication : Gopakumar G, K, H. Babu, D, M., SS, G., and GR, S. Subrahmany, “Cytopathological image analysis using deep-learning networks in microfluidic microscopy”, Journal of the Optical Society of America A, vol. 34, no. 1, pp. 111-121, 2017

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