博彩导航

报告题目：Biological Imaging Informatics and Clinical Pathology Diagnosis–the Development in the Digital and AI Era

时间：2024年1月2日（周二）15:00-16:30

地点：博彩导航 至真楼404智慧教室

主讲人：余维淼

个人简介：

Speaker:Prof. YU Weimiao (Principal Scientist), A*STAR

     Head of Computational Digital Pathology Lab

     Bioinformatics Institute (BII)

     Head of Computational and Molecular Pathology Lab

     Institute of Molecular and Cell Biology (IMCB)

     Agency of Science Technology and Research (A*STAR)

     Adjunct Professor

     School Artificial Intelligence,

     Nanjing University of Information Science and Technology (NUIST)

     Adjunct Professor

     School Biological Science (SBS),

     Nanyang Technological University

余维淼（首席科学家）

     计算数字病例实验室-生物信息研究院

     计算分子病理实验室-分子细胞研究院

     新加坡科技局

     兼职客座教授

     人工智能学院-南京信息科技大学

     客座讲席教授

     生物科学学院-南洋理工大学

Abstract

In recent years, the advancements in image-based deep learning technologies have significantly propelled the field of Artificial Intelligence (AI)-based pathological diagnosis. Numerous promising research outcomes have emerged. However, the translation of these advancements into real-world clinical applications remains limited. The primary hindrance lies in an array of challenges and bottlenecks, encompassing both technical and non-technical aspects. The field still lacks certain critical components required for comprehensive development. In this sharing session, we will conduct a thorough review of the field's current status while scrutinizing the obstacles, challenges, and bottlenecks impeding progress. Additionally, we will delve into understanding the ecosystem that underpins the industry's growth. Three pivotal concepts, optimization, generalization andvariation,serve as linchpins for successfully deploying AI models in clinical settings. I will share fundamental principles that can guide us in achieving optimization and generalization when designing our studies. Furthermore, we will explore five essential and practical solutions aimed at expediting the development of clinically applicable AI models in pathological diagnosis. This will set the stage for the accelerated growth of AI-based pathological diagnosis in the years to come.

 近年来，基于图像的深度学习技术的快速发展显著推动了基于人工智能（AI）的病理诊断领域。已经出现了许多有希望的研究成果。然而，这些进展转化为实际临床应用仍然有限。主要障碍在于这个领域还存在一系列的挑战和瓶颈，包括技术和非技术方面。该领域仍缺乏某些必要的关键组件以实现全面发展。在本次分享中，我们将对该领域的当前状态进行较全面回顾，同时仔细分析阻碍进展的难题、挑战和瓶颈。此外，我们还将深入了解支撑该行业增长的生态系统。优化性，泛化性和差异性是三个关键概念，它们共同作为在临床环境中成功部署AI模型的关键支柱。我将分享一些基本原则，以指导我们在设计研究时如何实现优化和泛化。此外，我们将探讨五种旨在加速发展适用于临床的AI病理诊断模型的必要和实用解决方案。这将为今后几年基于人工智能的病理诊断的加速发展奠定基础。

Bio

Dr. Yu Weimiao obtained his Ph.D. from National University of Singapore (NUS) in 2007, majoring in image processing and machine vision. He joined Agency of Science, Technology and Research(A*STAR) in 2007 and moved his research position to Institute of Molecular and Cell Biology (IMCB) in 2010. He is currently heading Computational Digital Pathology Lab (CDPL) in Bioinformatics Institute (BII) and Computational Molecular & Pathology Lab (CMPL) in IMCB to deepen and extend the R&D with clinical and industrial partners. His research interests are Computational Biomedical Image Analysis and Quantitative Imaging Informatics in biomedical image. Dr. YU’s research outcomes were published in top international peer reviewed journals, such as Nature Cell Biology, Nature Communication, Current Biology, etc. His previous research outcomes were successfully commercialized and made an impact in the diagnosis and drug discovery. To enhance the application of machine learning and AI in clinical diagnosis/prognosis, he founded a biotech company in Singapore, known A!maginostic Pte. Ltd. He established a world class platform for the immunodiagnosis at tissue level. The platform allows the researchers, clinicians and pharma to profile the patient immune signature for the purpose of diagnosis, prognosis and drug response study.

    余维淼博士于2007年获得新加坡国立大学（NUS）博士学位，主修图像处理和机器视觉。他于2007年加入新加坡科技研究局（A*STAR），并于2010年加入分子与细胞生物学研究所（IMCB）。他目前领导的生物信息学研究所（BII）的计算数字病理学实验室（CDPL）和IMCB的计算分子与病理学实验室（CMPL），旨在深化和扩展与临床和工业合作伙伴的研发。他的研究兴趣是计算生物医学图像分析和生物医学图像中的定量图像信息学。余博士的研究成果发表在国际顶级同行评审期刊上，如《自然细胞生物学》、《自然通讯》、《当代生物学》等。他以前的研究成果已成功商业化，并对诊断和药物发现产生了影响。为了加强机器学习和人工智能在临床诊断/预后中的应用，他在新加坡成立了一家名为A!maginostic Pte. Ltd的生物技术有限公司。他建立了一个世界级的组织水平免疫诊断平台。该平台允许研究人员、临床医生和制药公司分析患者的免疫特征，以进行诊断、预后和药物反应研究。