Multi-Scale Conditional Generative Adversarial Network for Small-Sized Lung Nodules Using Class Activation Region Influence Maximization

Automatic detection and classification of thoracic diseases using deep learning algorithms have many applications supporting radiologists' diagnosis and prognosis. However, in the medical field, the class-imbalanced problem is extremely common due to the differences in prevalence among diseases, making it difficult to develop these applications. Many GAN-based methods have been proposed to solve the class-imbalance problem on chest X-ray (CXR) data. However, these models have not been trained well for small-sized diseases because it is challenging to extract sufficient information with only a few pixels. In this paper, we propose a novel deep generative model called a class activation region influence maximization conditional generative adversarial network (CARIM-cGAN). The proposed network can control the target disease's presence, location, and size with a controllable conditional mask. We newly introduced class activation region influence maximization (CARIM) loss to maximize the probability of disease occurrence in the bounded region represented by a conditional mask. To demonstrate an enhanced generative performance, we conducted numerous qualitative and quantitative evaluations with the samples generated using a CARIM-cGAN. The results showed that our method has a better performance than other methods. In conclusion, because the CARIM-cGAN can generate high-quality samples based on information on the location and size of the disease, we can contribute to solving problems such as disease classification,-detection, and-localization, requiring a higher annotation cost.