Weakly supervised deep learning for detecting and counting dead cells in microscopy images

Siteng Chen, Ao Li, Kathleen Lasick, Julie Huynh, Linda Powers, Janet Roveda, Andrew Paek

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Counting dead cells is a key step in evaluating the performance of chemotherapy treatment and drug screening. Deep convolutional neural networks (CNNs) can learn complex visual features, but require massive ground truth annotations which is expensive in biomedical experiments. Counting cells, especially dead cells, with very few ground truth annotations remains unexplored. In this paper, we automate dead cell counting using a weakly supervised strategy. We took advantage of the fact that cell death is low before chemotherapy treatment and increases after treatment. Motivated by the contrast, we first design image level supervised only classification neural networks to detect dead cells. Based on the class response map in classification networks, we calculate a Dead Confidence Map (DCM) to specify confidence of each dead cell. Associated with peak clustering, local maximums in the DCM are used to count the number of dead cells. In addition, a biological experiment based weakly supervised data preparation strategy is proposed to minimize human intervention. We show classification performance compared to general purpose and cell classification networks, and report results for the image-level supervised counting task.

Original languageEnglish (US)
Title of host publicationProceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019
EditorsM. Arif Wani, Taghi M. Khoshgoftaar, Dingding Wang, Huanjing Wang, Naeem Seliya
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1737-1743
Number of pages7
ISBN (Electronic)9781728145495
DOIs
StatePublished - Dec 2019
Event18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019 - Boca Raton, United States
Duration: Dec 16 2019Dec 19 2019

Publication series

NameProceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019

Conference

Conference18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019
CountryUnited States
CityBoca Raton
Period12/16/1912/19/19

Keywords

  • Classification
  • Convolutional neural networks
  • Counting
  • Dead cells
  • Machine learning
  • Microscopy image
  • Weakly supervised learning

ASJC Scopus subject areas

  • Strategy and Management
  • Artificial Intelligence
  • Computer Science Applications
  • Decision Sciences (miscellaneous)
  • Signal Processing
  • Media Technology

Fingerprint Dive into the research topics of 'Weakly supervised deep learning for detecting and counting dead cells in microscopy images'. Together they form a unique fingerprint.

  • Cite this

    Chen, S., Li, A., Lasick, K., Huynh, J., Powers, L., Roveda, J., & Paek, A. (2019). Weakly supervised deep learning for detecting and counting dead cells in microscopy images. In M. A. Wani, T. M. Khoshgoftaar, D. Wang, H. Wang, & N. Seliya (Eds.), Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019 (pp. 1737-1743). [8999140] (Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMLA.2019.00282