Adversarial Domain Adaptation Network with Enhanced Feature Discriminability for Thyroid Ultrasound Images

Abstract

In recent years, computer-aided diagnosis technology has made breakthroughs in clinical medicine. However, due to the different models and configurations of ultrasound instruments, the thyroid ultrasound images collected by different medical centers have different visual characteristics. It leads to domain shift in multi-center data and the lower generalization of computer-aided diagnosis models. We consider this limitation may attribute to the minor inter-class differences in thyroid ultrasound images, resulting in confusion space before and after domain feature alignment. Therefore, we propose an adversarial domain adaptation network with enhanced feature discriminability method. Among them, the discriminative feature learning module strengthens the discriminability of the learned features, so that the class distribution in each domain presents a high cohesion and low coupling effect; the class alignment module reduces the distance between the same class samples across domains to achieve class alignment and further strengthen the discriminability of each class. Experiments show that our method outperforms other state-of-the-art algorithms on the internal thyroid ultrasound image dataset. The method can effectively improve the model's generalization ability on multi-center thyroid ultrasound images.

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