http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Study of COVID-Net applications
Samaneh Shamshiri,Insoo Sohn 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.11
In response to the widespread outbreak of COVID-19 all around the world, Computer-Aided diagnosis (CADs) applications, especially deep neural networks, have led to an efficient diagnosis of COVID-19 along with increased detection accuracy. To this purpose, in March 2020, COVID-Net was introduced: a tailored deep convolutional neural network designed for detecting COVID-19 cases from chest X-ray images, which achieves an impressive accuracy rate. Since COVID-Net was launched as an open-source model, it was available fully for the research community. In this paper, the methodology and results of the latest release of COVID-Net deep learning models are summarized and discussed.
Security methods for AI based COVID-19 analysis system : A survey
Samaneh Shamshiri,손인수 한국통신학회 2022 ICT Express Vol.8 No.4
Rapid progress and widespread outbreak of COVID-19 have caused devastating influence on the health systems all around the world. The importance of countermeasures to tackle this problem lead to widespread use of Computer Aided Diagnosis (CADs) applications using deep neural networks. The unprecedented success of machine learning techniques, especially deep learning networks in medical images, have led to their recent prominence in improving efficient diagnosis of COVID-19 with increased detection accuracy. However, recent studies in the field of security of AI-based systems revealed that these deep learning models are vulnerable to adversarial attacks. Adversarial examples generated by attack algorithms are not recognizable by the human eye and can easily deceive the state-of-the-art deep learning models, therefore they threaten security-critical learning applications. In this paper, the methodology, results and concerns of recent works on robustness of AI based COVID-19 systems are summarized and discussed. We explore important security concerns related to deep neural networks and review current state-of-the-art defense methods to prevent performance degradation.
Study of Datasets in AI Based Medical Informatics
Samaneh Shamshiri,Insoo Sohn(손인수) 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.6
In recent years, Artificial intelligence (AI) has played a significant role in health informatics and medical applications to promote early detections, disease diagnosis, and referral managements. In terms of classification, labeling, training process, dataset size, and algorithm validation of AI, uncourtly, data is the first step to developing any treatment or detecting tools. This paper reviews some dataset analytic role and data driven models in medical informatics and investigated the two first datasets from CT scan images and chest X-ray images for detecting COVID-19 by AI models.