XPGAN: X-Ray Projected Generative Adversarial Network For Improving Covid-19 Image Classification

• Main Authors: Quan, Tran Minh, Thanh, Huynh Minh, Huy, Ta Duc, Chanh, Nguyen Do Trung, Anh, Nguyen Thi Phuong, Vu, Phan Hoan, Nam, Nguyen Hoang, Tuong, Tran Quy, Dien, Vu Minh, Van Giang, Bui, Trung, Bui Huu, Truong, Steven Quoc Hung
• Format: Conference Proceeding
• Language: English
• Subjects: Chest X-ray; Classification; Computed tomography; COVID-19; Digitally Reconstructed Radiographs; Generative Adversarial Network; Generative adversarial networks; Neural networks; Solid modeling; Three-dimensional displays; Training
• ISSN: 1945-8452
• DOI: 10.1109/ISBI48211.2021.9434159

This work aims to fight against the current outbreak pandemic by developing a method to classify suspected infected COVID-19 cases. Driven by the urgency, due to the vastly increased number of patients and deaths worldwide, we rely on situationally pragmatic chest X-ray scans and state-of-the-art deep learning techniques to build a robust diagnosis for massive screening, early detection, and in-time isolation decision making. The proposed solution, X-ray Projected Generative Adversarial Network (XPGAN), addresses the most fundamental issue in training such a deep neural network on limited human-annotated datasets. By leveraging the generative adversarial network, we can synthesize a large amount of chest X-ray images with prior categories from more accurate 3D Computed Tomography data, including COVID-19, and jointly train a model with a few hundreds of positive samples. As a result, XPGAN outperforms the vanilla DenseNet121 models and other competing baselines trained on the same frontal chest X-ray images.