ArticleScientific data2024
An ultrasonography of thyroid nodules dataset with pathological diagnosis annotation for deep learning.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A lightweight GhostYOLO framework with GhostDynamic Module and multi-scale attention for thyroid nodule diagnosis.Scientific reports · 2026Article
- Training the diagnostic artificial intelligence in thyroid sonography: how well is deep learning truly learning?Updates in surgery · 2026Article
- TN5000: An Ultrasound Image Dataset for Thyroid Nodule Detection and Classification.Scientific data · 2025Article
- An annotated heterogeneous ultrasound database.Scientific data · 2025Article
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12 authors.
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Abstract
Ultrasonography (US) of thyroid nodules is often time consuming and may be inconsistent between observers, with a low positivity rate for malignancy in biopsies. Even after determining the ultrasound Thyroid Imaging Reporting and Data System (TIRADS) stage, Fine needle aspiration biopsy (FNAB) is still required to obtain a definitive diagnosis. Although various deep learning methods were developed in medical field, they tend to be trained using TI-RADS reports as image labels. Here, we present a large US dataset with pathological diagnosis annotation for each case, designed for developing deep learning algorithms to directly infer histological status from thyroid ultrasound images. The dataset was collected from two retrospective cohorts, which consists of 8508 US images from 842 cases. Additionally, we explained three deep learning models used as validation examples using this dataset.
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