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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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Related Experiment Video

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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
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Texture analysis and multiple-instance learning for the classification of malignant lymphomas.

Marco Lippi1, Stefania Gianotti2, Angelo Fama3

  • 1Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, Italy; Artificial Intelligence Research and Innovation center, University of Modena and Reggio Emilia, Italy; InterMech Center, University of Modena and Reggio Emilia, Italy.

Computer Methods and Programs in Biomedicine
|November 4, 2019
PubMed
Summary
This summary is machine-generated.

Machine learning and positron emission tomography texture analysis can differentiate malignant lymphoma subtypes. This approach shows high accuracy in identifying specific cancers like Hodgkin's lymphoma, aiding in differential diagnosis.

Keywords:
Malignant lymphomasMultiple-instance learningTexture analysis

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Area of Science:

  • Oncology
  • Medical Imaging
  • Machine Learning

Background:

  • Malignant lymphomas are immune system cancers characterized by widespread enlarged lymph nodes.
  • Current diagnosis relies on single-site biopsies, while imaging provides a broader view.
  • Histological subtypes of lymphoma vary significantly.

Purpose of the Study:

  • To predict differential diagnosis of main malignant lymphoma subtypes using a data-driven approach.
  • To leverage multiple-instance learning algorithms and texture analysis from positron emission tomography (PET).
  • To explore texture features for distinguishing between lymphoma types.

Main Methods:

  • Utilized a multiple-instance learning framework with support vector machines and random forests.
  • Classifiers were applied at both single VOI (instance) and patient (bag) levels.
  • Evaluated on datasets of patients with diffuse large B cell lymphoma, follicular lymphoma, Hodgkin's lymphoma, and mantle cell lymphoma.

Main Results:

  • Achieved 97.0% sensitivity and 94.1% predictive positive value for Hodgkin's lymphoma identification.
  • Demonstrated that texture information from PET scans can be discriminating for certain lymphoma subtypes.
  • Results were obtained on a dataset of 60 patients, indicating feasibility with sufficient data.

Conclusions:

  • Texture analysis features from PET scans combined with multiple-instance machine learning are effective for discriminating malignant lymphoma subtypes.
  • This approach offers a promising tool for improving differential diagnosis in lymphoma.
  • Further research with larger datasets may enhance diagnostic capabilities.