Over the past decade, the use of deep learning has been widely increasing in the medical image diagnosis field. Deep learning-based methods' (DLMs) performance strongly relies on training data. Therefore, researchers often focus on collecting as much data as possible from different medical facilities or developing approaches to avoid the impact of inter-category imbalance (ICI), which means a difference in data quantity among categories. However, due to the ICI within each medical facility, medical data are often isolated and acquired in different settings among medical facilities, known as the issue of intra-source imbalance (ISI) characteristic. This imbalance also impacts the performance of DLMs but receives negligible attention. In this study, we study the impact of the ISI on DLMs by comparison of the version of a deep learning model that was trained separately by an intra-source imbalanced chest X-ray (CXR) dataset and an intra-source balanced CXR dataset for COVID-19 diagnosis. The finding is that using the intra-source imbalanced dataset causes a serious training bias, although the dataset has a good inter-category balance. In contrast, the deep learning model performed a reliable diagnosis when trained on the intra-source balanced dataset. Therefore, our study reports clear evidence that the intra-source balance is vital for training data to minimize the risk of poor performance of DLMs.

Department of Computer Science Faculty of Science University of South Bohemia in Ceske Budejovice 370 05 Ceske Budejovice Czech Republic

Department of General Engineering National Institute of Technology Sendai College Sendai 989 3128 Japan

Graduate School of Biomedical Engineering Tohoku University Sendai 980 8576 Japan

Institute of Development Aging and Cancer Tohoku University Sendai 980 8576 Japan

Tohoku University Graduate School of Medicine Tohoku University Sendai 980 8576 Japan

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