ArticleMedical image analysis2014
Accurate diagnosis of thyroid follicular lesions from nuclear morphology using supervised learning.
Article in Medical image analysis, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- A Novel Three-Stage AI-Assisted Approach for Accurate Differential Diagnosis and Classification of NIFTP and Thyroid Neoplasms.Endocrine pathology · 2025Article
- Breaking Barriers in Thyroid Cytopathology: Harnessing Deep Learning for Accurate Diagnosis.Bioengineering (Basel, Switzerland) · 2025Article
- Quantifying Nuclear Structures of Digital Pathology Images Across Cancers Using Transport-Based Morphometry.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2025Article
- Discovering the gene-brain-behavior link in autism via generative machine learning.Science advances · 2024Article
- 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
- Accuracy of Ultrasound Diagnosis of Thyroid Nodules Based on Artificial Intelligence-Assisted Diagnostic Technology: A Systematic Review and Meta-Analysis.International journal of endocrinology · 2022Article
- PARTITIONING SIGNAL CLASSES USING TRANSPORT TRANSFORMS FOR DATA ANALYSIS AND MACHINE LEARNING.Sampling theory, signal processing, and data analysis · 2021Article
- Machine learning methods for automated classification of tumors with papillary thyroid carcinoma-like nuclei: A quantitative analysis.PloS one · 2021Article
- Radial Basis Function Artificial Neural Network for the Investigation of Thyroid Cytological Lesions.Journal of thyroid research · 2020Article
- Artificial Intelligence for Personalized Medicine in Thyroid Cancer: Current Status and Future Perspectives.Frontiers in oncology · 2020Review
- Head and Neck Cancer Detection in Digitized Whole-Slide Histology Using Convolutional Neural Networks.Scientific reports · 2019Article
- Identifying Imaging Markers for Predicting Cognitive Assessments Using Wasserstein Distances Based Matrix Regression.Frontiers in neuroscience · 2019Article
- A TransportationJournal of mathematical imaging and vision · 2017Article
- Identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning.Scientific reports · 2017Article
- Optimal Mass Transport: Signal processing and machine-learning applications.IEEE signal processing magazine · 2017Article
- A continuous linear optimal transport approach for pattern analysis in image datasets.Pattern recognition · 2016Article
- The Radon Cumulative Distribution Transform and Its Application to Image Classification.IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · 2016Article
- Artificial Neural Networks as Decision Support Tools in Cytopathology: Past, Present, and Future.Biomedical engineering and computational biology · 2016Review
- Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review.IEEE reviews in biomedical engineering · 2016Review
Corrections and comments
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Authors and funding
8 authors.
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Abstract
Follicular lesions of the thyroid remain significant diagnostic challenges in surgical pathology and cytology. The diagnosis often requires considerable resources and ancillary tests including immunohistochemistry, molecular studies, and expert consultation. Visual analyses of nuclear morphological features, generally speaking, have not been helpful in distinguishing this group of lesions. Here we describe a method for distinguishing between follicular lesions of the thyroid based on nuclear morphology. The method utilizes an optimal transport-based linear embedding for segmented nuclei, together with an adaptation of existing classification methods. We show the method outputs assignments (classification results) which are near perfectly correlated with the clinical diagnosis of several lesion types' lesions utilizing a database of 94 patients in total. Experimental comparisons also show the new method can significantly outperform standard numerical feature-type methods in terms of agreement with the clinical diagnosis gold standard. In addition, the new method could potentially be used to derive insights into biologically meaningful nuclear morphology differences in these lesions. Our methods could be incorporated into a tool for pathologists to aid in distinguishing between follicular lesions of the thyroid. In addition, these results could potentially provide nuclear morphological correlates of biological behavior and reduce health care costs by decreasing histotechnician and pathologist time and obviating the need for ancillary testing.
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Registered trials
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