ArticleBioengineering (Basel, Switzerland)2025
Breaking Barriers in Thyroid Cytopathology: Harnessing Deep Learning for Accurate Diagnosis.
Article in Bioengineering (Basel, Switzerland), 2025. 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.
- Uncertainty-aware and interpretable deep learning in cytopathology: toward trustworthy AI-powered diagnostics.Virchows Archiv : an international journal of pathology · 2026Review
- From Pixels to Prediction: Developing Integrated AI Foundation Models for Personalized Thyroid Cancer Care.Cancers · 2026Review
- Cytological Classification Diagnosis for Thyroid Nodules via Multimodal Model Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Review
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Authors and funding
6 authors.
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Abstract
backgroundWe address the application of artificial intelligence (AI) techniques in thyroid cytopathology, specifically for diagnosing papillary thyroid carcinoma (PTC), the most common type of thyroid cancer.
methodsOur research introduces deep learning frameworks that analyze cytological images from fine-needle aspiration cytology (FNAC), a key preoperative diagnostic method for PTC. The first framework is a patch-level classifier referred as "TCS-CNN", based on a convolutional neural network (CNN) architecture, to predict thyroid cancer based on the Bethesda System (TBS) category. The second framework is an attention-based deep multiple instance learning (AD-MIL) model, which employs a feature extractor using TCS-CNN and an attention mechanism to aggregate features from smaller-patch-level regions into predictions for larger-patch-level regions, referred to as bag-level predictions in this context.
resultsThe proposed TCS-CNN framework achieves an accuracy of 97% and a recall of 96% for small-patch-level classification, accurately capturing local malignancy information. Additionally, the AD-MIL framework also achieves approximately 96% accuracy and recall, demonstrating that this framework can maintain comparable performance while expanding the diagnostic coverage to larger regions through patch aggregation.
conclusionsThis study provides a feasibility analysis for thyroid cytopathology classification and visual interpretability for AI diagnosis, suggesting potential improvements in patient outcomes and reductions in healthcare costs.
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