Presentation + Paper
11 August 2023 Hierarchical learning approaches for improving label-free single-cell classification in holographic microscopy
Author Affiliations +
Abstract
Machine learning in combination with microscopy is a well-established paradigm for the identification of cells target (e.g. sick cells) or for the statistical study of cells’ populations. In general, the accuracy in classifying single cells depends on the selected imaging modality, i.e., the more informative it is, the more performant the classifier is. Here we show that the combination of machine learning and holographic microscopy is an effective tool to achieve the above goal, thus allowing higher classification performances if compared to other standard microscopies. Moreover, by exploiting a priori information about the samples to identify, the classification performance can be further increased. We demonstrate this paradigm for the differential diagnosis of hereditary anemias, in which RBCs, imaged by holographic microscopy, are used to predict firstly if an anemia occurs, then which type of anemia among five phenotypes.
Conference Presentation
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Pasquale Memmolo, Vittorio Bianco, Roberta Russo, Immacolata Andolfo, Martina Mugnano, Francesco Merola, Lisa Miccio, Achille Iolascon, and Pietro Ferraro "Hierarchical learning approaches for improving label-free single-cell classification in holographic microscopy", Proc. SPIE 12622, Optical Methods for Inspection, Characterization, and Imaging of Biomaterials VI, 126220E (11 August 2023); https://doi.org/10.1117/12.2674836
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KEYWORDS
Microscopy

Machine learning

Holography

Image classification

Red blood cells

Cell phenotyping

Deep convolutional neural networks

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