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Classification of Epithelial Tissues: Overview01:22

Classification of Epithelial Tissues: Overview

Epithelial tissues are classified according to the shape of the cells and the number of cell layers formed. Cell shapes can be squamous (flattened and thin), cuboidal (square-like, as wide as it is tall), or columnar (rectangular, taller than it is wide). Additionally, the nucleus shape helps identify the type of epithelial cells. Squamous cells have flattened disc-shaped nuclei, cuboidal cells have spherical nuclei, and columnar cells have elongated nuclei.
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Stratified epithelium consists of several stacked layers of cells. They provide the durability to withstand constant physical and chemical attacks. Stratified epithelium is named after the shape of the most apical layer of cells. Stratified squamous epithelium is the most common type found in the human body. In this tissue, the apical cells are squamous, whereas the basal layer contains either columnar or cuboidal cells. The basal cells divide to form new daughter cells, which gradually become...
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Connective Tissue Proper
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Breast Cancer Tissue Classification from Multiple Annotators using Chained Deep Learning Approaches.

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    Summary
    This summary is machine-generated.

    Computer-aided diagnosis (CAD) for breast cancer classification faces challenges with limited labeled data. A multi-annotator approach using generalized cross-entropy loss (GCEDL) demonstrated robustness, nearing expert performance.

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    Area of Science:

    • Medical Imaging
    • Computational Pathology
    • Artificial Intelligence in Medicine

    Background:

    • Breast cancer diagnosis relies on biopsy, which can be subjective and lead to inter-specialist disagreement.
    • Computer-aided diagnosis (CAD) systems aim to improve efficiency and reduce diagnosis time but require substantial labeled data.
    • Crowdsourcing offers a solution for acquiring labels from diverse annotator expertise levels.

    Purpose of the Study:

    • To evaluate a multi-annotator approach for breast cancer tissue classification.
    • To compare the performance of two loss functions: cross-s-entropy (RCDNN) and generalized cross-entropy (GCEDL).
    • To assess the robustness of models in a scenario with varying annotator expertise.

    Main Methods:

    • Utilized a breast cancer tissue dataset annotated by both expert and non-expert annotators.
    • Implemented and compared two distinct loss functions: RCDNN and GCEDL.
    • Evaluated model performance in a multi-annotator setting.

    Main Results:

    • The multi-annotator scenario presents significant challenges in achieving high diagnostic accuracy.
    • The generalized cross-entropy loss function (GCEDL) model exhibited superior robustness compared to RCDNN.
    • The GCEDL model achieved performance comparable to a gold-standard single-annotator model.

    Conclusions:

    • Multi-annotator data can be leveraged for breast cancer classification, but careful model selection is crucial.
    • GCEDL demonstrates promise for handling noisy labels from diverse annotators in medical image analysis.
    • Future work should focus on further optimizing models for multi-annotator datasets to enhance CAD system reliability.