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Classification of Leukocytes01:30

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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.
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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Classification of Systems-I01:26

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Force Classification01:22

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Related Experiment Video

Updated: Oct 3, 2025

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
11:38

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

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Automated human cell classification in sparse datasets using few-shot learning.

Reece Walsh1, Mohamed H Abdelpakey2, Mohamed S Shehata2

  • 1Department of Computer Science, Mathematics, Physics and Statistics, University of British Columbia, Kelowna, Canada. reece.walsh@ubc.ca.

Scientific Reports
|February 22, 2022
PubMed
Summary
This summary is machine-generated.

Few-shot learning techniques show poor performance in automated human cell classification due to data scarcity. Future research should focus on improving out-of-domain robustness for better accuracy.

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Area of Science:

  • Computational biology
  • Machine learning

Background:

  • Automating human cell classification is crucial for expediting analysis, often requiring trained professionals.
  • Deep learning models show promise but demand extensive data, which is scarce for human cell datasets, leading to low performance.

Purpose of the Study:

  • Investigate the feasibility of few-shot learning (FSL) techniques to reduce data requirements for accurate human cell classification.
  • Evaluate current state-of-the-art FSL methods, explore backbone architecture and training scheme variations, and propose future research directions.

Main Methods:

  • Evaluated state-of-the-art FSL techniques on human cell classification, training on non-medical data and testing on out-of-domain medical datasets.
  • Experimented with EPNet, modifying backbone architectures, data augmentation, and training schemes to assess impact on performance.
  • Analyzed performance drops and identified limitations of current FSL approaches for medical image classification.

Main Results:

  • State-of-the-art FSL techniques experienced at least a 30% decrease in test accuracy when transitioning from non-medical to human cell datasets.
  • EPNet and Reptile showed the best performance on the BCCD and HEp-2 datasets, respectively.
  • Even with modifications, EPNet's accuracy dropped from 88.66% on non-medical data to a maximum of 44.13% on medical data.

Conclusions:

  • Few-shot learning, in its current form, performs poorly on human cell classification tasks.
  • Modifying existing network architectures did not effectively improve performance.
  • Future research should prioritize enhancing out-of-domain robustness through optimization-based or self-supervised FSL techniques.