Automatic classification method of thyroid pathological images using multiple magnification factors

Abstract

Abstract Thyroid cancer is the most common form of endocrine malignancy, and the informative pathological images are critical for thyroid cancer risk stratification, prognosis and treatment guidance. Deep learning methods have achieved promising results on pathology image classification benchmarks. However, due to the complexity of thyroid carcinoma pathological images and the lack of labeled data, there are few researches study on the auto-classification of thyroid cancer. Inspired by the diagnostic process of pathologists, this paper proposes an active classification method for papillary thyroid carcinoma (PTC) pathological images classification to divide thyroid pathological images into PTC and normal thyroid pathological images. We employ the attention mechanism to combine pathological images with different magnification factors, which imitates the diagnosis procedures of thyroid cancer under the microscope. At the same time, we utilize the uncertainty and representative information provided by the convolutional neural network to determine the most valuable samples for annotation that can reduce the labeling cost. Besides, a pathological image dataset named VIP-TCHis is conducted from thyroid tissues of 55 real cases. The experimental results show that our method can obtain good performance of PTC recognition on the VIP-TCHis dataset.