Journal Article

Machine learning methods for automated classification of tumors with papillary thyroid carcinoma-like nuclei: A quantitative analysis

Böhland, Moritz, Mikut, Ralf, Hagenmeyer, Veit, Thompson, Lester D. R., Perner, Sven, Tharun, Lars, Scherr, Tim, Reischl, Markus

2021

Overview

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.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Accuracy / Analysis / Artificial neural networks / Automation / Biology and Life Sciences / Classification / Computer and Information Sciences / Datasets / Decision making / Deep learning / Engineering and Technology / Feature extraction / Gene expression / Genetic aspects / Identification and classification / Image classification / Image processing / Image segmentation / Informatics / Learning algorithms / Machine learning / Medicine and Health Sciences / Neural networks / Nuclei / Papillary thyroid carcinoma / Pathology / People and Places / Quantitative analysis / Thyroid / Thyroid cancer / Thyroid gland / Tumors