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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.
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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Related Experiment Video

Updated: Jul 29, 2025

Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT
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Clinically Labeled Contrastive Learning for OCT Biomarker Classification.

Kiran Kokilepersaud, Stephanie Trejo Corona, Mohit Prabhushankar

    IEEE Journal of Biomedical and Health Informatics
    |May 22, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new method for training AI models using clinical data as pseudo-labels to improve medical image analysis, specifically for detecting biomarkers in ophthalmology. This approach enhances diagnostic accuracy from optical coherence tomography scans.

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    Area of Science:

    • Ophthalmology
    • Medical Imaging
    • Artificial Intelligence

    Background:

    • Medical image analysis often requires extensive labeled data, which can be scarce.
    • Clinical data is often more abundant than specialized biomarker labels.
    • Optical coherence tomography (OCT) scans in ophthalmology show correlations between clinical values and biomarker structures.

    Purpose of the Study:

    • To develop a novel strategy for positive and negative set selection in contrastive learning for medical images.
    • To leverage readily available clinical data as pseudo-labels for training AI models.
    • To improve the direct classification of disease indicators from OCT scans using biomarker labels.

    Main Methods:

    • Utilized clinical data as pseudo-labels to select instances for supervised contrastive loss training.
    • Trained a backbone network to align representation space with clinical data distribution.
    • Fine-tuned the network using biomarker-labeled data with cross-entropy loss.
    • Proposed a method using a linear combination of clinical contrastive losses.

    Main Results:

    • Achieved performance improvements of up to 5% in total biomarker detection AUROC.
    • Demonstrated the effectiveness of using clinical data for pre-training in a novel setting with varying biomarker granularity.
    • Successfully trained a network to learn representations aligned with clinical data distribution.

    Conclusions:

    • The proposed strategy effectively utilizes clinical data for contrastive learning in medical imaging.
    • This method enhances the ability to classify disease indicators directly from OCT scans.
    • The approach offers a promising direction for improving AI-driven diagnostics in ophthalmology with limited biomarker data.