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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >RCoNet: Deformable Mutual Information Maximization and High-Order Uncertainty-Aware Learning for Robust COVID-19 Detection
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RCoNet: Deformable Mutual Information Maximization and High-Order Uncertainty-Aware Learning for Robust COVID-19 Detection

机译：rconet：可变形的互信息最大化和高阶的不确定性感知用于强大的Covid-19检测的学习

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摘要

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 RCoNetks 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(s)(k) 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.
机译：2019年的2019年冠状病毒（Covid-19）感染在全球范围内传播，目前是世界各地的主要医疗保健挑战。胸部计算断层扫描（CT）和X射线图像得到了很好的认可，是临床covid-19疾病诊断的两种有效技术。由于成像时间更快，并且成本远低于CT，优选检测胸部X射线（CXR）图像中的Covid-19，以便有效诊断，评估和治疗。然而，考虑到Covid-19和肺炎之间的相似性，具有深层特征的CXR样品分布在附近边界附近的界限很容易被从有限训练数据中学到的超班位错误分类。此外，Covid-19的大多数现有方法侧重于预测的准确性和忽略不确定性估计，这在处理嘈杂的数据集时尤为重要。为了缓解这些问题，我们提出了一个名为Rconetk的新型网络，用于强大的CoVID-19检测，采用可变形的互信息最大化（DEIM），混合高阶时刻特征（MHMF），以及多因素不确定性感知学习（MUL）。通过DEIM，输入数据和相应的潜在表示之间的互信息（MI）可以估计并最大化以捕获紧凑和解散的代表特征。同时，MHMF可以充分探索使用高阶统计和提取复杂分布中的辨别特征在医学成像中的歧视。最后，MUL为每个CXR图像创建多个并行丢弃网络，以评估不确定性，从而防止由数据中的噪声引起的性能下降。实验结果表明，Rconet（k）在几个指标上实现了15 134个原始CXR图像的开源Covidx数据集的最先进的性能。至关重要的是，我们的方法显示比数据存在噪声的现有方法更有效。

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