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Machine Learning of Discriminative Gate Locations for Clinical Diagnosis
- Source :
- Cytometry. Part A : the journal of the International Society for Analytical Cytology, vol 97, iss 3, Cytometry
- Publication Year :
- 2019
- Publisher :
- Wiley, 2019.
-
Abstract
- High‐throughput single‐cell cytometry technologies have significantly improved our understanding of cellular phenotypes to support translational research and the clinical diagnosis of hematological and immunological diseases. However, subjective and ad hoc manual gating analysis does not adequately handle the increasing volume and heterogeneity of cytometry data for optimal diagnosis. Prior work has shown that machine learning can be applied to classify cytometry samples effectively. However, many of the machine learning classification results are either difficult to interpret without using characteristics of cell populations to make the classification, or suboptimal due to the use of inaccurate cell population characteristics derived from gating boundaries. To date, little has been done to optimize both the gating boundaries and the diagnostic accuracy simultaneously. In this work, we describe a fully discriminative machine learning approach that can simultaneously learn feature representations (e.g., combinations of coordinates of gating boundaries) and classifier parameters for optimizing clinical diagnosis from cytometry measurements. The approach starts from an initial gating position and then refines the position of the gating boundaries by gradient descent until a set of globally‐optimized gates across different samples are achieved. The learning procedure is constrained by regularization terms encoding domain knowledge that encourage the algorithm to seek interpretable results. We evaluate the proposed approach using both simulated and real data, producing classification results on par with those generated via human expertise, in terms of both the positions of the gating boundaries and the diagnostic accuracy. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.
- Subjects :
- 0301 basic medicine
Histology
Computer science
Immunology
Population
discriminative gates
Diagnostic accuracy
Gating
Machine learning
computer.software_genre
Pathology and Forensic Medicine
Machine Learning
automated gating
03 medical and health sciences
0302 clinical medicine
Discriminative model
cancer diagnosis
Computational Article
Humans
supervised machine learning
education
education.field_of_study
business.industry
flow cytometry
Cell Biology
Computational Articles
Statistical classification
030104 developmental biology
030220 oncology & carcinogenesis
Clinical diagnosis
chronic lymphocytic leukemia
Domain knowledge
Biochemistry and Cell Biology
Artificial intelligence
business
Gradient descent
computer
Algorithms
Subjects
Details
- ISSN :
- 15524930 and 15524922
- Volume :
- 97
- Database :
- OpenAIRE
- Journal :
- Cytometry Part A
- Accession number :
- edsair.doi.dedup.....33641fe86d23c54c5e5a8690f445f87d