Automatic Quantification of COVID-19 Pulmonary Edema by Self-supervised Contrastive Learning.
Liang Z, Xue Z, Rajaraman S, Feng Y, Antani SK
In: Xue Z, et al. Medical Image Learning with Limited and Noisy Data. MILLanD 2023. Lecture Notes in Computer Science, vol 14307. Springer, Cham. https://doi.org/10.1007/978-3-031-44917-8_12.
Abstract:
We proposed a self-supervised machine learning method to automatically rate the severity of pulmonary edema in the frontal chest X-ray radiographs (CXR) which could be potentially related to COVID-19 viral pneumonia. For this we use the modified radiographic assessment of lung edema (mRALE) scoring system. The new model was first optimized with the simple Siamese network (SimSiam) architecture where a ResNet-50 pretrained by ImageNet database was used as the backbone. The encoder projected a 2048-dimension embedding as representation features to a downstream fully connected deep neural network for mRALE score prediction. A 5-fold cross-validation with 2,599 frontal CXRs was used to examine the new model’s performance with comparison to a non-pretrained SimSiam encoder and a ResNet-50 trained from scratch. The mean absolute error (MAE) of the new model is 5.05 (95%CI 5.03–5.08), the mean squared error (MSE) is 66.67 (95%CI 66.29–67.06), and the Spearman's correlation coefficient (Spearman ρ) to the expert-annotated scores is 0.77 (95%CI 0.75–0.79). All the performance metrics of the new model are superior to the two comparators (P < 0.01), and the scores of MSE and Spearman ρ of the two comparators have no statistical difference (P > 0.05). The model also achieved a prediction probability concordance of 0.811 and a quadratic weighted kappa of 0.739 with the medical expert annotations in external validation. We conclude that the self-supervised contrastive learning method is an effective strategy for mRALE automated scoring. It provides a new approach to improve machine learning performance and minimize the expert knowledge involvement in quantitative medical image pattern learning.
Liang Z, Xue Z, Rajaraman S, Feng Y, Antani SK. Automatic Quantification of COVID-19 Pulmonary Edema by Self-supervised Contrastive Learning.
In: Xue Z, et al. Medical Image Learning with Limited and Noisy Data. MILLanD 2023. Lecture Notes in Computer Science, vol 14307. Springer, Cham. https://doi.org/10.1007/978-3-031-44917-8_12.
URL: https://doi.org/10.1007/978-3-031-44917-8_12