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Classification of Leukocytes

Classification of Leukocytes

Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...

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Classification of Neural Stem Cell Activation State In Vitro using Autofluorescence

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Unknown cell class distinction via neural network based scattering snapshot recognition.

Gaia Cioffi¹, David Dannhauser¹, Domenico Rossi²

¹Interdisciplinary Research Centre on Biomaterials (CRIB) and Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale, Università degli Studi di Napoli "Federico II", Piazzale Tecchio 80, 80125 Naples, Italy.

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|October 19, 2023

Summary

This study introduces an open-set neural network for classifying unknown cell types in life sciences. The method accurately detects unknown cells and quantifies prediction uncertainty, improving deep learning applications.

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Area of Science:

Life Sciences
Biotechnology
Computational Biology

Background:

Neural network image classification is crucial in life sciences but struggles with unknown data.
Current closed-set models misclassify unknown images, necessitating advanced approaches.
Open-set classification offers a solution by distinguishing known from unknown data distributions.

Purpose of the Study:

To implement and evaluate an open-set classification approach for living cell image analysis.
To distinguish between known monoblast cell classes and an unknown tumoral cell line.
To assess the impact of experimental errors and optimize neural network hyperparameters for unknown cell detection.

Main Methods:

Applied an open-set neural network framework to scattering snapshots of living cells.
Targeted four known monoblast cell classes and one unknown tumoral monoblast cell line.
Investigated experimental sample errors and optimized neural network hyperparameters.

Main Results:

The open-set approach successfully distinguished between known and unknown cell classes.
Achieved high accuracy in detecting the unknown tumoral cell line.
Demonstrated robustness against experimental sample noise.
The neural network revealed measurement uncertainty in cell predictions.

Conclusions:

Open-set classification is effective for identifying unknown cell types in life science imaging.
The developed method is robust to experimental noise, a key requirement for biological applications.
The approach provides valuable insights into prediction uncertainty for single-cell classification.
This framework has broad applicability across various single-cell analysis tasks.