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Related Concept Videos

The Thyroid Gland01:23

The Thyroid Gland

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The thyroid gland is a small, butterfly-shaped gland located in the neck and covers the anterior surface of the trachea. The gland has two lateral lobes connected by a thin tissue mass called the isthmus. Internally, each lobe comprises many small spherical structures known as thyroid follicles, surrounded by a network of blood vessels.
The follicles have a central cavity lined by simple cuboidal to squamous epithelial cells called follicular cells. These cells produce the glycoprotein...
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Synthesis and Regulation of Thyroid Hormones01:20

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Low blood levels of the thyroid hormones — triiodothyronine (T3) and thyroxine (T4) — signal the hypothalamus to release the thyrotropin-releasing hormone (TRH). TRH then reaches the pituitary gland and stimulates the release of thyroid-stimulating hormone(TSH) into the bloodstream.
Upon reaching the thyroid gland, TSH stimulates the follicular cells' active uptake of iodide ions from the blood. The ions diffuse to the apical surface of the cells and are oxidized to iodine. The...
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The Parathyroid Glands00:59

The Parathyroid Glands

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The two pairs of parathyroid glands embedded within the posterior surface of the thyroid gland are restricted by a dense capsule around them. These glands comprise two distinct cell populations—parathyroid oxyphil and parathyroid principal cells- pivotal in calcium homeostasis.
Oxyphil cells, whose functions remain elusive, emerge during late puberty, adding a layer of complexity to the parathyroid gland's intricacies. In contrast, principal parathyroid cells undertake a vital role by...
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Related Experiment Video

Updated: Jun 6, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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Multiple-Instance Learning for thyroid gland disease classification: A hands-on experience.

Daniil Lysukhin1, Andrey Varlamov1, Boris Yakimov2

  • 1Endocrinology Research Centre, Moscow, Russia.

Computers in Biology and Medicine
|November 29, 2024
PubMed
Summary

This study developed an artificial intelligence (AI) model using weakly-annotated real-world data to diagnose thyroid cancer from whole slide images (WSIs). The AI model successfully distinguished between benign and malignant thyroid conditions at the patient level.

Keywords:
Artificial intelligenceHistopathologyMultiple Instance LearningThyroid cancerWhole-slide images

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Area of Science:

  • Digital pathology
  • Computational diagnostics
  • Artificial intelligence in medicine

Background:

  • Morphological diagnosis of thyroid neoplasms is time-consuming and requires specialized expertise.
  • Current artificial intelligence (AI) research often relies on meticulously curated datasets, which are costly and complex to prepare.
  • Weakly-annotated, real-world data presents an alternative for AI model development in diagnostics.

Purpose of the Study:

  • To develop and evaluate machine learning models using weakly-annotated, real-world data for thyroid gland neoplasm diagnosis.
  • To assess the performance of a Multiple-Instance Learning (MIL) model trained on patient-level annotations.
  • To identify potential data quality issues affecting AI model performance and compare annotation strategies.

Main Methods:

  • Development of a Multiple-Instance Learning (MIL) model trained on patient-level annotations from 1102 patients and 5104 whole slide images (WSIs).
  • Utilized "real-world" data without selective preprocessing.
  • Compared classification accuracy using detailed slide-level versus coarse patient-level annotations on a smaller dataset (36 cases, 91 WSIs).

Main Results:

  • The MIL model achieved an average test set F1-Score of 0.85 (±0.05) in discriminating benign from malignant thyroid conditions at the patient level.
  • This represents the first reported AI model trained on patient-level data without prior labeling refinement.
  • Identified data quality pitfalls, such as resection margin dye, that can lead to model overfitting.
  • Detailed annotations significantly improved classification performance on smaller datasets.

Conclusions:

  • AI models can effectively diagnose thyroid neoplasms using weakly-annotated, real-world data, reducing the need for extensive manual annotation.
  • Patient-level annotations are feasible for training diagnostic AI models, though data quality must be carefully managed.
  • The findings highlight the potential of AI to streamline pathological workflows and improve diagnostic efficiency.