SR-AnoGAN: You Never Detect Alone. Super Resolution in Anomaly Detection (Student Abstract) - Cheon, Minjong (Proceedings of the AAAI Conference on Artificial Intelligence, 2024)
Despite the advance in deep learning algorithms, implementing supervised learning algorithms in medical datasets is difficult owing to the medical data’s properties. This paper proposes SR-AnoGAN, which could generate higher resolution images and conduct anomaly detection more efficiently than AnoGAN. The most distinctive part of the proposed model is incorporating CNN and SRGAN into AnoGAN for reconstructing high-resolution images. Experimental results from X-ray datasets(pneumonia, covid-19) verify that the SR-AnoGAN outperforms the previous AnoGAN model through qualitative and quantitative approaches. Therefore, this paper shows the possibility of resolving data imbalance problems prevalent in the medical field, and proposing more precise diagnosis.