ArXiv Preprint
Background and objective: COVID-19 and its variants have caused significant
disruptions in over 200 countries and regions worldwide, affecting the health
and lives of billions of people. Detecting COVID-19 from chest X-Ray (CXR)
images has become one of the fastest and easiest methods for detecting COVID-19
since the common occurrence of radiological pneumonia findings in COVID-19
patients. We present a novel high-accuracy COVID-19 detection method that uses
CXR images. Methods: Our method consists of two phases. One is self-supervised
learning-based pertaining; the other is batch knowledge ensembling-based
fine-tuning. Self-supervised learning-based pretraining can learn distinguished
representations from CXR images without manually annotated labels. On the other
hand, batch knowledge ensembling-based fine-tuning can utilize category
knowledge of images in a batch according to their visual feature similarities
to improve detection performance. Unlike our previous implementation, we
introduce batch knowledge ensembling into the fine-tuning phase, reducing the
memory used in self-supervised learning and improving COVID-19 detection
accuracy. Results: On two public COVID-19 CXR datasets, namely, a large dataset
and an unbalanced dataset, our method exhibited promising COVID-19 detection
performance. Our method maintains high detection accuracy even when annotated
CXR training images are reduced significantly (e.g., using only 10% of the
original dataset). In addition, our method is insensitive to changes in
hyperparameters. Conclusions: The proposed method outperforms other
state-of-the-art COVID-19 detection methods in different settings. Our method
can reduce the workloads of healthcare providers and radiologists.
Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama
2022-12-19
