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

 Kyeongjin Ann  ;  Yeonggul Jang  ;  Hackjoon Shim  ;  Hyuk-Jae Chang 
 IEEE ACCESS, Vol.9 : 139426-139437, 2021-09 
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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.
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1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Yonsei Biomedical Research Center (연세의생명연구원) > 1. Journal Papers
Yonsei Authors
Shim, Hack Joon(심학준)
Chang, Hyuk-Jae(장혁재) ORCID logo https://orcid.org/0000-0002-6139-7545
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