報告題目：Novel Segmentation and Quantification Methods for CT-based COVID-19 Diagnosis and Prognosis
報告時間：2021年6月22日 13:00-15:00
報告地點：伟德国际BETVlCTOR中心校區計算機樓A521
報 告 人：高欣 教授
報告人簡介：
高欣，博士，現任沙特阿蔔杜拉國王科技大學（KAUST）計算機科學系教授，并擔任KAUST計算生物學研究中心副主任、KAUST智慧健康中心副主任、KAUST結構和功能生物信息學研究小組負責人。先後于2004年在清華大學計算機系獲得學士學位，2009年在加拿大滑鐵盧大學計算機學院獲得博士學位。2009年10月至2010年9月，在美國卡耐基梅隆大學計算機學院雷恩計算生物學中心擔任雷恩學者。高欣博士的研究焦點主要集中在計算機科學與生物學的交叉領域。在計算機科學領域，他領導的研究團隊主要緻力于開發與深度學習、概率圖形模型、内核方法和矩陣分解相關的機器學習理論和方法。在生物信息學領域，他的研究團隊主要緻力于構建計算模型、研發機器學習技術、設計高效的算法，以解決從生物序列分析到三維結構确定，到功能注釋，再到了解和控制複雜生物網絡中的分子行為，以及最近的生物醫療和健康領域中的關鍵開放問題。在Nature Communications、Nature Catalysis、PNAS、NAR、PLOS Computational Biology、Bioinformatics、TPAMI、TNNLS、ISMB、RECOMB、ICLR、IJCAI、AAAI、KDD等國際重要期刊和會議上發表論文250餘篇，同時擔任Genomics，Proteomics & Bioinformatics、BMC Bioinformatics、Quantitative Biology、IEEE/ACM Transactions on Computational Biology and Bioinformatics等期刊的副主編，以及Methods和Frontiers in Molecular Bioscience等期刊的客座主編。（實驗室網頁：https://sfb.kaust.edu.sa）
報告内容簡介：
COVID-19 has caused a global pandemic and become the most urgent threat to the entire world. Tremendous efforts and resources have been invested in developing diagnosis, yet the pandemic is still undergoing.
Despite the various, urgent advances in developing artificial intelligence (AI)-based computer-aided systems for CT-based COVID-19 diagnosis, most of the existing methods can only perform classification, whereas the state-of-the-art segmentation methods require high levels of human intervention. In this talk, I will introduce our work on a fully-automatic, rapid, accurate, and machine-agnostic method that can segment and quantify the infection regions on CT scans from different sources. Our method is founded upon three innovations: 1) An embedding method that projects any arbitrary CT scan to a same, standard space, so that the trained model becomes robust and generalizable; 2) The first CT scan simulator for COVID-19, by fitting the dynamic change of real patients’ data measured at different time points, which greatly alleviates the data scarcity issue; and 3) A novel deep learning algorithm to solve the large-scene-small-object problem, which decomposes the 3D segmentation problem into three 2D ones, and thus reduces the model complexity by an order of magnitude and, at the same time, significantly improves the segmentation accuracy. Comprehensive experimental results over multi-country, multi-hospital, and multi-machine datasets demonstrate the superior performance of our method over the existing ones and suggest its important application value in combating the disease. I will finally introduce our ongoing work on developing full interpretable AI models to “see the unseen” from the CT scans of COVID-19 survivors to diagnose the long-term sequela of COVID-19.
主辦單位：伟德国际BETVlCTOR
伟德国际BETVlCTOR軟件學院
伟德国际BETVlCTOR計算機科學技術研究所
符号計算與知識工程教育部重點實驗室
仿真技術教育部重點實驗室
網絡技術及應用軟件教育部工程研究中心
伟德国际BETVlCTOR國家級計算機實驗教學示範中心
