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21st International Conference on Image Analysis and Processing , ICIAP 2022 ; 13374 LNCS:508-519, 2022.
Article in English | Scopus | ID: covidwho-2013964


The outbreak of the COVID-19 pandemic considerably increased the workload in hospitals. In this context, the availability of proper diagnostic tools is very important in the fight against this virus. Scientific research is constantly making its contribution in this direction. Actually, there are many scientific initiatives including challenges that require to develop deep algorithms that analyse X-ray or Computer Tomography (CT) images of lungs. One of these concerns a challenge whose topic is the prediction of the percentage of COVID-19 infection in chest CT images. In this paper, we present our contribution to the COVID-19 Infection Percentage Estimation Competition organised in conjunction with the ICIAP 2021 Conference. The proposed method employs algorithms for classification problems such as Inception-v3 and the technique of data augmentation mixup on COVID-19 images. Moreover, the mixup methodology is applied for the first time in radiological images of lungs affected by COVID-19 infection, with the aim to infer the infection degree with slice-level precision. Our approach achieved promising results despite the specific constrains defined by the rules of the challenge, in which our solution entered in the final ranking. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

IEEE/CVF International Conference on Computer Vision (ICCVW) ; : 423-432, 2021.
Article in English | Web of Science | ID: covidwho-1701651


COVID-19 diagnosis using chest x-ray (CXR) imaging has a greater sensitivity and faster acquisition procedures than the Real-Time Polimerase Chain Reaction (RT-PCR) test, also requiring radiology machinery that is cheap and widely available. To process the CXR images, methods based on Deep Learning (DL) are being increasingly used, often in combination with data augmentation techniques. However, no method in the literature performs data augmentation in which the augmented training samples are processed collectively as a multi-channel image. Furthermore, no approach has yet considered a combination of attention-based networks with Convolutional Neural Networks (CNN) for COVID-19 detection. In this paper, we propose the first method for COVID-19 detection from CXR images that uses an innovative self-augmentation scheme based on reinforcement learning, which combines all the augmented images in a 3D deep volume and processes them together using a novel non-local deep CNN, which integrates convolutional and attention layers based on non-local blocks. Results on publicly-available databases exhibit a greater accuracy than the state of the art, also showing that the regions of CXR images influencing the decision are consistent with radiologists' observations.