ArticleNature communications2025
Improving AI models for rare thyroid cancer subtype by text guided diffusion models.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Genomics and Multi-Omics to Guide Clinical Management in Thyroid Cancer.International journal of molecular sciences · 2026Review
- Machine-Learning Microfluidic Minute-Scale Microorganism Metrics Monitoring(M6).Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Diffusion Models in Oncology: a Review of Current Applications.Current oncology reports · 2026Review
- Roadmap for Representative Artificial Intelligence Models for Thyroid Cancer.The Laryngoscope · 2026Review
- Ultrasound features of thyroid follicular adenoma and follicular carcinoma: A narrative review.The Journal of international medical research · 2026Review
- Dual-database Bibliometric Analysis Combined with Gephi-based Network Visualization of Artificial Intelligence Applications in the Identification and Diagnosis of Thyroid Space-occupying Lesions.Current medical imaging · 2026Article
- Research on pedestrian recognition in complex scenarios based on data augmentation using large language models.Scientific reports · 2025Article
- Integrated local and global Ki67 profiling for prognostic stratification and therapeutic guidance in high-grade neuroendocrine neoplasms: a multicenter validation study.International journal of surgery (London, England) · 2025Article
- Cell Viability, Drug Screening, and Mechanism Study Under Mild Phototherapy Integrated Chemotherapy (PIC).Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- AI-driven enhancements in rare disease diagnosis and support system optimization.Intractable & rare diseases research · 2025Article
- Bioorthogonal optimized virus immuno-nanomedicine (BOVIN).Nature communications · 2025Article
- Explainable multimodal deep learning for predicting thyroid cancer lateral lymph node metastasis using ultrasound imaging.Nature communications · 2025Article
Corrections and comments
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Authors and funding
19 authors.
Funding
Abstract
Artificial intelligence applications in oncology imaging often struggle with diagnosing rare tumors. We identify significant gaps in detecting uncommon thyroid cancer types with ultrasound, where scarce data leads to frequent misdiagnosis. Traditional augmentation strategies do not capture the unique disease variations, hindering model training and performance. To overcome this, we propose a text-driven generative method that fuses clinical insights with image generation, producing synthetic samples that realistically reflect rare subtypes. In rigorous evaluations, our approach achieves substantial gains in diagnostic metrics, surpasses existing methods in authenticity and diversity measures, and generalizes effectively to other private and public datasets with various rare cancers. In this work, we demonstrate that text-guided image augmentation substantially enhances model accuracy and robustness for rare tumor detection, offering a promising avenue for more reliable and widespread clinical adoption.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.