ArticlePloS one2021
Machine learning methods for automated classification of tumors with papillary thyroid carcinoma-like nuclei: A quantitative analysis.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 35 citations in OpenAlex.
- Systematic review of generative adversarial networks (GANs) in cell microscopy: Trends, practices, and impact on image augmentation.PloS one · 2025Pooled it
- From Bench-to-Bedside: How Artificial Intelligence is Changing Thyroid Nodule Diagnostics, a Systematic Review.The Journal of clinical endocrinology and metabolism · 2024Pooled it
- PathoTiroid dataset: Indonesian collection (PTIC)-a histopathology image dataset for papillary thyroid carcinoma.BMC research notes · 2026Article
- Thyroid cancer detection and classification using spectral imaging and artificial intelligence.Scientific reports · 2026Article
- MiThyCA: A Computational Pathology Pipeline for the Identification of Microscopic Foci of Papillary Thyroid Carcinoma-Like Nuclear Features with AI in Whole-Slide Histological Images.Endocrine pathology · 2025Article
- Application of data science methods, including machine learning, in the classification of focal lesions in the thyroid gland.Journal of ultrasonography · 2025Article
- A Novel Three-Stage AI-Assisted Approach for Accurate Differential Diagnosis and Classification of NIFTP and Thyroid Neoplasms.Endocrine pathology · 2025Article
- Prognostic Models Using Machine Learning Algorithms and Treatment Outcomes of Papillary Thyroid Carcinoma Variants.Cancer reports (Hoboken, N.J.) · 2024Article
- Article
- Comparative performance analysis of binary variants of FOX optimization algorithm with half-quadratic ensemble ranking method for thyroid cancer detection.Scientific reports · 2023Article
- A Framework for Detecting Thyroid Cancer from Ultrasound and Histopathological Images Using Deep Learning, Meta-Heuristics, and MCDM Algorithms.Journal of imaging · 2023Article
- Artificial Intelligence in Thyroidology: A Narrative Review of the Current Applications, Associated Challenges, and Future Directions.Thyroid : official journal of the American Thyroid Association · 2023Review
- Trends in AI-powered Classification of Thyroid Neoplasms Based on Histopathology Images - a Systematic Review.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2023Article
- Applications of machine and deep learning to thyroid cytology and histopathology: a review.Frontiers in oncology · 2023Review
- KaIDA: a modular tool for assisting image annotation in deep learning.Journal of integrative bioinformatics · 2022Article
- Membrane marker selection for segmenting single cell spatial proteomics data.Nature communications · 2022Article
- Deep Learning-Based Recognition of Different Thyroid Cancer Categories Using Whole Frozen-Slide Images.Frontiers in bioengineering and biotechnology · 2022Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
When approaching thyroid gland tumor classification, the differentiation between samples with and without "papillary thyroid carcinoma-like" nuclei is a daunting task with high inter-observer variability among pathologists. Thus, there is increasing interest in the use of machine learning approaches to provide pathologists real-time decision support. In this paper, we optimize and quantitatively compare two automated machine learning methods for thyroid gland tumor classification on two datasets to assist pathologists in decision-making regarding these methods and their parameters. The first method is a feature-based classification originating from common image processing and consists of cell nucleus segmentation, feature extraction, and subsequent thyroid gland tumor classification utilizing different classifiers. The second method is a deep learning-based classification which directly classifies the input images with a convolutional neural network without the need for cell nucleus segmentation. On the Tharun and Thompson dataset, the feature-based classification achieves an accuracy of 89.7% (Cohen's Kappa 0.79), compared to the deep learning-based classification of 89.1% (Cohen's Kappa 0.78). On the Nikiforov dataset, the feature-based classification achieves an accuracy of 83.5% (Cohen's Kappa 0.46) compared to the deep learning-based classification 77.4% (Cohen's Kappa 0.35). Thus, both automated thyroid tumor classification methods can reach the classification level of an expert pathologist. To our knowledge, this is the first study comparing feature-based and deep learning-based classification regarding their ability to classify samples with and without papillary thyroid carcinoma-like nuclei on two large-scale datasets.
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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.