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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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Updated: Jan 15, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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Thyroid pathology image classification via multi-scale feature fusion and multi-instance learning.

Xiangzhi Li1,2,3, Guanxin Liu4, Mengmeng Sun1,2

  • 1Guangxi Key Laboratory on Precise Prevention and Treatment for Thyroid Tumour, The Second Affiliated Hospital of Guangxi University of Science and Technology, Liuzhou, China.

Diagnostic Pathology
|October 15, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an AI tool for thyroid cancer diagnosis, improving accuracy and reducing pathologist workload. The novel weakly supervised multiple-instance learning framework shows promising results on internal and external datasets.

Keywords:
Digital pathological imagesFusionMulti featureMulti-instance learningMulti-scale

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Area of Science:

  • Computational pathology
  • Artificial intelligence in diagnostics
  • Digital pathology

Background:

  • Global thyroid cancer incidence is rising.
  • Traditional diagnosis is time-consuming and requires expert interpretation.
  • Need for efficient and accurate diagnostic tools.

Purpose of the Study:

  • Develop an auxiliary diagnostic tool for thyroid cancer.
  • Reduce pathologist workload and enhance diagnostic accuracy.
  • Investigate the efficacy of a novel multi-feature fusion architecture.

Main Methods:

  • Utilized 543 whole slide images (WSIs) for model development.
  • Employed a multi-feature fusion architecture combining RetCCL, iBOT, and DINO embeddings.
  • Evaluated stain normalization and multi-scale analysis within four multiple-instance learning (MIL) frameworks (CLAM-SB, CLAM-MB, DTFD, LA-MIL).
  • Validated on an independent set of 128 WSIs.

Main Results:

  • Stain normalization, multi-scale, and multi-feature fusion significantly improved classification performance.
  • Internal dataset (10-fold CV): AUC improved by 2.8% (0.9900), accuracy by 7.2% (0.9594) over baseline.
  • External validation dataset: AUC 0.9584, accuracy 0.9070, precision 0.9247, F1-score 0.9348.
  • Demonstrated robust and reliable performance on both datasets.

Conclusions:

  • Proposed a weakly supervised MIL framework for thyroid cancer diagnosis.
  • The method integrates multi-scale analysis and cross-model feature fusion.
  • Showed promising and consistent results across internal and external datasets.
  • Potential to assist pathologists, especially in resource-limited settings, pending further validation.