RCoNet: Deformable Mutual Information Maximization and High-Order Uncertainty-Aware Learning for Robust COVID-19 Detection

Abstract

The novel 2019 Coronavirus (COVID-19) infection has spread worldwide and is currently a major healthcare challenge around the world. Chest computed tomography (CT) and X-ray images have been well recognized to be two effective techniques for clinical COVID-19 disease diagnoses. Due to faster imaging time and considerably lower cost than CT, detecting COVID-19 in chest X-ray (CXR) images is preferred for efficient diagnosis, assessment, and treatment. However, considering the similarity between COVID-19 and pneumonia, CXR samples with deep features distributed near category boundaries are easily misclassified by the hyperplanes learned from limited training data. Moreover, most existing approaches for COVID-19 detection focus on the accuracy of prediction and overlook uncertainty estimation, which is particularly important when dealing with noisy datasets. To alleviate these concerns, we propose a novel deep network named RCoNet k s for robust COVID-19 detection which employs Deformable Mutual Information Maximization (DeIM), Mixed High-order Moment Feature (MHMF), and Multiexpert Uncertainty-aware Learning (MUL). With DeIM, the mutual information (MI) between input data and the corresponding latent representations can be well estimated and maximized to capture compact and disentangled representational characteristics. Meanwhile, MHMF can fully explore the benefits of using high-order statistics and extract discriminative features of complex distributions in medical imaging. Finally, MUL creates multiple parallel dropout networks for each CXR image to evaluate uncertainty and thus prevent performance degradation caused by the noise in the data. The experimental results show that RCoNet k s achieves the state-of-the-art performance on an open-source COVIDx dataset of 15 134 original CXR images across several metrics. Crucially, our method is shown to be more effective than existing methods with the presence of noise in the data.

COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention

“…These two types of images are yielded because different organs have different ability to absorb X-rays, and then the abnormalities can be detected based on the contrast in these images. Comparing with CXR, CT has multiple levels of grayscale; but CXR is a more accessible, affordable, and popular method for diagnosing lung infections [ 8 ]. Furthermore, artificial visual interpretation of these images is time-consuming and relies heavily on the subjective judgment of the physician.…”

Chest X ray and cough sample based deep learning framework for accurate diagnosis of COVID-19

“…Moreover, various hyper-parameters like dropout rate, activation functions, and the number of hidden layers, learning rates of deep neural network-based models have also been performed to solve the overfitting problem. The number of epochs versus the decay of model loss has been investigated to rule out the possibility of overfitting [21] , [22] . These performance metrics are based on a confusion matrix (shown in Tables 4 , 6 , and Fig.…”

“… A: IEEE-8023 CXR - Cohen dataset [21] , B: Pneumonia and normal chest X-ray, C: Shenzhen CXR with Masks, D: Montgomery county CXR images, E: COVIDGR 1.0, W Women, M Men, N Negative cases, P positive D 426P(239W 187M) used training model. …”

Uncertainty-aware convolutional neural network for COVID-19 X-ray images classification

“…They have tested uncertainty estimation techniques like softmax scors, deterministic uncertainty quantification and MC dropout. Similarly, Dong et al [ 46 ] proposed a deep learning framework named RCoNetk for COVID-19 detection and uncertainty estimation. In which, they have utilized Deformable Mixed High-order Moment Feature, Mutual Information Maximization, and Multiexpert Uncertainty-aware learning.…”

“…The majority of existing approaches [ [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] ] have not provided information about the uncertainty associated with their predictions. Only a few studies [ [44] , [45] , [46] ] have provided their model uncertainty information. Whereas an uncertainty estimation of the model is very important in the medical domain for a reliable and safe CAD system.…”