随着新冠病毒疫情的不断变异和扩散，COVID-19快速精准诊断成为抗疫、防疫的广泛性难题。为此，广东技术师范大学积极投身于抗疫、防疫的技术研究。近2年来，威尼斯wnsr888(中国)官方网站院长赵慧民教授、广东技术师范大学达之学者任金昌教授科研团队通过胸部X-超像素图像分割处理及其AI空间域的智能计算研究，建立了Covid-19分类网络SC2Net （Segmentation-based COVID-19 classification network）。SC2Net由肺部图像分割网络-CLSeg和空间注意力网络-SANet组成，旨在解决新冠肺炎感染的精准诊断问题。在COVIDGR 1.0(西班牙Hospital Universitario Clínico San Cecilio, Granada,Spain)数据库实验表明，SC2Net平均诊断率超过84.23%，比现在诊断使用的 FuCiT-Net 和COVID-SDNet方法准确率提高了3%-4.8%。相关研究成果2020-2022年分别发表在领域内顶级期刊IEEE生物医学与健康信息杂志（IEEE Journal of Biomedical and Health Informatics, 影响因子10.90）, IEEE Transactions on Cybernetics, 52(7): 6158-6169, 2022.(影响因子为11.448)。目前，该团队积极与广州医科大学附属医院合作，进行临床应用实验。
【1】Huimin Zhao∗, Zhenyu Fang∗, Jinchang Ren, Calum Maclellam,et al.SC2Net: a novel segmentation-based classification network for detection of Covid-19 in chest X-ray images, IEEE Journal of Biomedical and Health Informatics, 26(8): 4032-4043, 2022.(Top,SCI一区，CS=10.9)
【2】J.CRen∗, Yan Y, H.MZhao*, Ma P, J.Zabalza, Z. Hussain,H.K. Li. A novel intelligent computational approach to model epidemiological trends and assess the impact of nonpharmacolo-gical interventions for COVID-19 [J]. IEEE journal of biomedical and health informatics,IEEE journal of biomedical and health informatics,24(12):3551-3563,2020.(Top,SCI一区，CS=10.9)
Abstract:The pandemic of COVID-19 has become a global crisis in public health, which has led to a massive number of deaths and severe economic degradation. To suppress the spread of COVID-19, accurate diagnosis at an early stage is crucial. As the popularly used real-time reverse transcriptase polymerase chain reaction (RT-PCR) swab test can be lengthy and inaccurate, chest screening with radiography imaging is still preferred. However, due to limited image data and the difficulty of the early-stage diagnosis, existing models suffer from ineffective feature extraction and poor network convergence and optimisation. To tackle these issues, a segmentation-based COVID-19 classification network, namely SC2Net, is proposed for effective detection of the COVID-19 from chest x-ray (CXR) images. The SC2Net consists of two subnets: a COVID-19 lung segmentation network (CLSeg), and a spatial attention network (SANet). In order to supress the interference from the background, the CLSeg is first applied to segment the lung region from the CXR. The segmented lung region is then fed to the SANet for classification and diagnosis of the COVID-19. For performance evaluation, the COVIDGR 1.0 dataset is used, which is a high-quality dataset with various severity levels of the COVID-19. Experimental results have shown that, our SC2Net has an average accuracy of 84.23% and an average F1 score of 81.31% in detection of COVID-19, outperforming several state-of-the-art approaches.
【3】G Sun, H Fu, J Ren*, A Zhang, J Zabalza, X Jia, H Zhao, “SpaSSA: Superpixelwise adaptive SSA for unsupervised spatial-spectral feature extraction in hyperspectral image”, IEEE Transactions on Cybernetics, 52(7): 6158-6169, 2022.(影响因子为11.448，SCI一区.TOP期刊)
Abstract:Singular spectral analysis (SSA) has recently been successfully applied to feature extraction in hyperspectral image (HSI), including conventional (1-D) SSA in spectral domain and 2-D SSA in spatial domain. However, there are some drawbacks, such as sensitivity to the window size, high computational complexity under a large window, and failing to extract joint spectral–spatial features. To tackle these issues, in this article, we propose superpixelwise adaptive SSA (SpaSSA), that is superpixelwise adaptive SSA for exploiting local spatial information of HSI. The extraction of local (instead of global) features, particularly in HSI, can be more effective for characterizing the objects within an image. In SpaSSA, conventional SSA and 2-D SSA are combined and adaptively applied to each superpixel derived from an oversegmented HSI. According to the size of the derived superpixels, either SSA or 2-D singular spectrum analysis (2D-SSA) is adaptively applied for feature extraction, where the embedding window in 2D-SSA is also adaptive to the size of the superpixel. Experimental results on the three datasets have shown that the proposed SpaSSA outperforms both SSA and 2D-SSA in terms of classification accuracy and computational complexity. By combining SpaSSA with the principal component analysis (SpaSSA-PCA).
【4】He Sun, Jinchang Ren*, Huimin Zhao*, Peter Yuen, Julius Tschannerl,“Novel gumbel-softmax trick enabled concrete autoencoder with entropy constraints for unsupervised hyperspectral band selection”,IEEE Trans. Geoscience and Remote Sensing, 60（1-13）, 2022. ( Top,SCI二区，影响因子7.72)
Abstract:With the great success of deep learning (DL)-based models recently, a robust unsupervised band selection (UBS) neural network is highly desired, particularly due to the lack of sufficient ground truth information to train the DL networks. Existing DL models for band selection either depend on the class label information or have unstable results via ranking the learned weights. To tackle these challenging issues, in this article, we propose a Gumbel-Softmax (GS) trick enabled concrete autoencoder-based UBS framework (CAE-UBS) for HSI, in which the learning process is featured by the introduced concrete random variables and the reconstruction loss. By searching from the generated potential band selection candidates from the concrete encoder, the optimal band subset can be selected based on an information entropy (IE) criterion. The robust performance on four publicly available datasets has validated the superiority of our CAE-UBS framework in the classification of the HSIs.