Artificial Intelligence in Cytopathology: A Neural Network to Identify Papillary Carcinoma on Thyroid Fine-Needle Aspiration Cytology Smears

• Published in: Journal of pathology informatics Vol. 9; no. 1; p. 43
• Main Authors: Sanyal, Parikshit, Mukherjee, Tanushri, Barui, Sanghita, Das, Avinash, Gangopadhyay, Prabaha
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.01.2018; Wolters Kluwer India Pvt. Ltd; Medknow Publications & Media Pvt. Ltd; Medknow Publications & Media Pvt Ltd; Elsevier
• Subjects: Arrays; Artificial intelligence; Artificial neural networks; Automation; Cellular biology; Classification; Cytology; Datasets; Diagnostic systems; fine-needle aspiration cytology; Image classification; Learning theory; Lesions; Machine learning; Medical imaging; Microphotographs; neural network; Neural networks; Object recognition; Original; Programming languages; Software; thyroid; Thyroid cancer; Thyroid gland; Training; cytology; fine-needle aspiration cytology; image classification; thyroid; Artificial intelligence; neural network

Introduction: Fine-needle aspiration cytology (FNAC) for identification of papillary carcinoma thyroid is a moderately sensitive and specific modality. The present machine learning tools can correctly classify images into broad categories. Training software for recognition of papillary thyroid carci...