KEYNOTE 1

Time: 20 August 2025      13:40~14:40

  • Biography
    Prof. Yang C. Fann earned his Ph.D. in Computational Chemistry from Temple University. Currently, he holds the position of Director for the Intramural IT and Bioinformatics Program at the National Institute of Neurological Disorders and Stroke (NINDS), which is part of the National Institutes of Health (NIH). Additionally, he serves as Clinical Informatics for the Office of Intramural Research. In these roles, he plays a pivotal role in addressing specific clinical informatics challenges and issues within the NIH Intramural Research Program (IRP). Prof. Fann has also overseen the development of numerous IT systems and extensive biomedical databases to facilitate data sharing, foster collaboration, and support innovative research endeavors.
  • Selected Awards and Honors
    2015 NIH CIT Director Award on Biomedical Research System Innovations
    2014 HHS Green Champion Leadership Award
    2014 NIH Director Award for Porter Neuroscience Building Operation
    2013 NIH Director Award for Intramural Research Program Leadership Team
    2012 NIH OD Honor Award on “I am Intramural Campaign”
    2012 NIH Director’s Award for Leveraging Efficiencies in Scientific Administration Team
    2010 HHS Secretary’s First Innovation Award
    2010 NINDS/NIH Director’s Award for Neurological Common Data Elements Project
  • Research Interests
    Dr. Fann’s current research interests are computational biology, bioinformatics, clinical research informatics, and applying information technology such as machine learning and artificial intelligence to advance translational biomedical research.
  • Selected Publications
    • Shih-Sheng Chang, Ching-Ting Lin, Wei-Chun Wang, Kai-Cheng Hsu, Ya-Lun Wu, Chia-Hao Liu, Yang C. Fann*; “Optimizing Ensemble U-Net Architectures for Robust Coronary Vessel Segmentation in Angiographic Images”; Scientific Reports, 14, 6640 (2024). https://doi.org/10.1038/s41598-024-57198-5
    • Ching-Heng Lin, Yi-An Chen, Jiann-Shing Jeng, Yu Sun, Cheng-Yu Wei, Po-Yen Yeh, Wei-Lun Chang, Yang C. Fann,*, Kai-Cheng Hsu*, Jiunn-Tay Lee, and Taiwan Stroke Registry Investigators; “Predicting Ischemic Stroke Patients’ Prognosis Changes using Machine Learning in a Nationwide Stroke Registry”; Med Biol Eng Comput (2024). https://doi.org/10.1007/s11517-024-03073-4
    • Chen KG, Johnson KR, Park K, Maric D, Yang F, Liu WF, Fann YC, Mallon BS, Robey PG; “Resistance to Naïve and Formative Pluripotency Conversion in RSeT Human Embryonic Stem Cells”; bioRxiv, 2024 Feb 17:2024.02.16.580778. doi: 10.1101/2024.02.16.580778, PMID: 38410444
    • Marco Egle, Wei-Chun Wang, Yang C Fann*, Michelle C Johansen, Jiunn-Tay Lee, Chung-Hsin Yehe, Chih- Hao Jason Lin, Jiann-Shing Jeng, Yu Sun, Li-Ming Lien , Jiunn-Tay Lee, Taiwan Stroke Registry Investigators, Rebecca F Gottesman*; “Sex Differences in the Role of Multimorbidity on Post-Stroke Disability: The Taiwan Stroke Registry”; Neurology, 2024;102:e209140. doi:10.1212/WNL.0000000000209140
    • Yen-Jung Chiu, Chao-Chun Chuang, Yu-Tai Wang, Lin-Chi Yeh, Romel Edwardo Rudon, Kuan-Wei Lin, Wei-Jong Yang, Yang C Fann, Pau-Choo Chung; “FLAg: An automated client-independent federated learning system on HPC for digital pathology slice training”; IEEE Artificial Intelligence, 2023, 314-315
    • Hendrick Gao-Min Lim, Yang C Fann, Yuan-Chii Gladys Lee; “COWID: An Efficient Cloud-Based Genomics Workflow for Scalable Identification of SARS-CoV-2”, Briefings in Bioinformatics, 2023, 1-12, https://doi.org/10.1093/bib/bbad280
    • Yuan-Chii Lee, Fang-Ning Chou, Szu-Yu Tung, Hsiu-Chu Chou, Tsui-Ling Ko, Yang C. Fann, Shu-Hui Juan *; “Tumoricidal activity of simvastatin in synergy with RhoA inactivation in antimigration of clear cell renal cell carcinoma cells”, Int J Mol Sci. 2023 Jun 4;24(11):9738. doi: 10.3390/ijms24119738. PMID: 37298689; PMCID: PMC10253741.
    • Ching-Heng Lin, Kai-Cheng Hsu, Chih-Kuang Liang, Tsong-Hai Lee, Ching-Sen Shih. and Yang C. Fann*; ” Accurately Identifying Cerebroarterial Stenosis from Angiography Reports Using Natural Language Processing Approaches”; Artificial Intelligence in Diagnostics 2022, 12, p1882. https://doi.org/10.3390/diagnostics12081882
    • Hendrick Gao-Min Lim, Shih-Hsin Hsiao, Yang C. Fann and Yuan-Chii Gladys Lee; “Robust Mutation Profiling of SARS-CoV-2 Variants from Multiple Raw Illumina Sequencing Data with Cloud Workflow”; Genes 2022, 13(4), 686; https://doi.org/10.3390/genes13040686
    • Hui-Ju Chang; Mei-Yu Lai; Chen-Hsin Chen; Yang C. Fann, Ueng-Cheng Yang “High BRCA1 gene expression increases the risk of early distant metastasis in ER+ breast cancers”, Scientific Reports, 12, 77 (2022). https://doi.org/10.1038/s41598-021-03471-w
    • Yang, L.-Y.; Tsai, M.-Y.; Juan, S.-H.; Chang, S.-F.; Yu, C.-T.R.; Lin, J.-C.; Johnson, K.R.; Lim, H.G.-M.; Fann, Y.C.; Lee, Y.-C.G. “Exerting the Appropriate Application of Methylprednisolone in Acute Spinal Cord Injury Based on Time Course Transcriptomics Analysis”; Int. J. Mol. Sci. 2021, 22, 13024. https://doi.org/10.3390/ijms222313024

KEYNOTE 2

Time: 21 August 2025      13:40~14:40

  • Biography
    Dr. Martel received a BSc in Physics from King’s College, University of London, UK in 1987 and a PhD in Medical Physics from the University of Sheffield, UK in 1992. She spent 11 years as a Medical Physicist in the UK before moving to Canada in 2003. She is currently a Senior Scientist in the Physical Sciences platform at Sunnybrook Research Institute and the Tory Family Chair in Oncology. She is also a Vector Faculty Affiliate. Her research program is focused on medical image and digital pathology analysis, particularly on applications of machine learning for segmentation, diagnosis, and prediction/prognosis. In 2006 she co-founded Pathcore, a software company developing complete workflow solutions for digital pathology.

    Dr Martel is an active member of the medical image analysis community and is a fellow of the MICCAI Society which represents engineers and computer scientists working in this field. She has served as a board member of MICCAI and is on the editorial board of the journal Medical Image Analysis.
  • Research Interests
    Self-supervised methods in medical imaging: It is expensive and time consuming to generate sufficient labelled data to train a machine learning model. We are exploring the use of self-supervised and contrastive learning approaches to pretrain models using large unlabelled datasets. We were the first group to demonstrate that resnet models pretrained on pathology images using SimCLR are able to learn downstream computational pathology tasks more accurately and with a fraction of the labels required by models pretrained using imageNet and the resnet model we released publicly has been widely downloaded and used.

    AI applied to breast cancer: We have several CIHR funded studies underway to train AI models for risk prediction from MRI images, predict the presence of occult invasive cancer from both digital pathology and mammography images and to predict recurrence free survival from digital pathology images and other clinical data.
  • Selected Publications
    • Chen, J., Cheung, H. M. C., Karanicolas, P. J., Coburn, N. G., Martel, G., Lee, A., Patel, C., Milot, L., & Martel, A. L. (2023). A radiomic biomarker for prognosis of resected colorectal cancer liver metastases generalizes across MRI contrast agents. Frontiers in Oncology, 13, 898854. https://doi.org/10.3389/fonc.2023.89885
    • Srinidhi, C. L., Kim, S. W., Chen, F.-D., & Martel, A. L. (2022). Self-supervised driven consistency training for annotation efficient histopathology image analysis. Medical Image Analysis, 75, 102256. https://doi.org/10.1016/j.media.2021.102256
    • Ciga, O., Xu, T., & Martel, A. L. (2022). Self supervised contrastive learning for digital histopathology. Machine Learning with Applications, 7, 100198. https://doi.org/10.1016/j.mlwa.2021.100198
    • Kuling, G., Curpen, B., & Martel, A. L. (2022). BI-RADS BERT and Using Section Segmentation to Understand Radiology Reports. Journal of Imaging, 8(5), 131. https://doi.org/10.3390/jimaging8050131
    • Ciga, O., Xu, T., Nofech-Mozes, S., Noy, S., Lu, F., & Martel, A. L. (2021). Overcoming the limitations of patch-based learning to detect cancer in whole slide images. Scientific Reports, 11(1), 8894. https://doi.org/10.1038/s41598-021-88494-z
    • Petrick, N., Akbar, S., Cha, K. H., Nofech-Mozes, S., Sahiner, B., Gavrielides, M. A., Kalpathy-Cramer, J., Drukker, K., Martel, A. L., & BreastPathQ Challenge Group, for the. (2021). SPIE-AAPM-NCI BreastPathQ challenge: an image analysis challenge for quantitative tumor cellularity assessment in breast cancer histology images following neoadjuvant treatment. Journal of Medical Imaging, 8(03), 034501. https://doi.org/10.1117/1.JMI.8.3.034501

KEYNOTE 3(Invitation)

Time: 22 August 2025      13:40~14:40

  • Biography
    Dr. Jenq-Neng Hwang received the BS and MS degrees, both in electrical engineering from the National Taiwan University, Taipei, Taiwan, in 1981 and 1983 separately. He then received his Ph.D. degree from the University of Southern California. In the summer of 1989, Dr. Hwang joined the Department of Electrical and Computer Engineering (ECE) of the University of Washington in Seattle, where he has been promoted to Full Professor since 1999. He served as the Associate Chair for Research from 2003 to 2005, and from 2011-2015. He also served as the Associate Chair for Global Affairs from 2015-2020. He is currently the International Programs Lead in the ECE Department. He is the founder and co-director of the Information Processing Lab., which has won several AI City Challenges awards in the past years. He has written more than 400 journal, conference papers and book chapters in the areas of machine learning, multimedia signal processing, computer vision, and multimedia system integration and networking (my Google citation), including an authored textbook on “Multimedia Networking: from Theory to Practice,” published by Cambridge University Press. Dr. Hwang has close working relationship with the industry on artificial intelligence and machine learning.
  • Selected Awards and Honors
    • Winner of both UAV-based Multi-Object Tracking with Reidentification and USV-based Multi-Object Tracking track at the 2nd Workshop on Maritime Computer Vision (MaCVi) in WACV 2024.
    • Winner of Track 1: Multi-Camera People Tracking, AI City Challenge 2023, IEEE/CVF CVPR 2023.
    • 3rd place of SportsMoT Challenge on Multi-actor Tracking, ECCV 2022
    • Winner of Video Track (both MOTChallenge-STEP and KITTI-STEP dataset) in the 6th BMTT Challenge (in conjunction with ICCV 2021)
    • 3rd place of Camera-View Track in the ICCV 2021 Multi-camera Multiple People Tracking Workshop
    • Winners of IEEE CVPR 5th BMTT MOTChallenge Workshop: Multi-Object Tracking and Segmentation, 2020
    • Winners of IEEE CVPR annual AI City Challenge 2018, 2019 and the corresponding reports
  • Research Interests
    He has specialized in Smart City, Self-driving Car, 5G V2X, Machine Learning, and Artificial intelligence.
  • Selected Publications
    • Maysam Orouskhani, Negar Firoozeh, Huayu Wang, Yan Wang, Hanrui Shi, Weijing Li, Beibei Sun, Jianjian Zhang, Xiao Li, Huilin Zhao, Mahmud Mossa-Basha, Jenq-Neng Hwang, Chengcheng Zhu, “Morphology and Texture-Guided Deep Neural Network for Intracranial Aneurysm Segmentation in 3D TOF-MRA.” accepted by the Neuroinformatics, August 2024.
    • Xiaoyan Jiang, Hangyu Tao, Jenq-Neng Hwang, Zhijun Fang, “A Multiscale Coarse-to-Fine Human Pose Estimation Network With Hard Keypoint Mining,” IEEE Trans. on Systems, Man and Cybernetics, Systems, November 2023Shenghao Hao, P. Liu, Y. Zhan, K. Jin, Z. Liu, M. Song, J.-N. Hwang, G. Wang, “DIVOTrack: A Novel Dataset and Baseline Method for Cross-View Multi-Object Tracking in DIVerse Open Scenes”, accepted by International Journal of Computer Vision (IJCV), 2023.
    • Hung-Min Hsu, Yizhou Wang, Cheng-Yen Yang, Jenq-Neng Hwang, Hoang Le Uyen Thuc, Kwang-Ju Kim, “Learning Temporal Attention based Keypoint-guided Embedding for Gait Recognition,” accepted by the special issue on “Biometrics at a distance in the Deep Learning era,” the IEEE Journal of Selected Topics in Signal Processing, April 2023.
    • Zheng Li, Caili Guo, Zerun Feng, Jenq-Neng Hwang, Zhongtian Du, “Integrating Language Guidance into Image-Text Matching for Correcting False Negatives,” accepted by IEEE Trans. on Multimedia, March 2023.
    • F. Tian, Yongbin Gao, Zhijun Fang, Yuming Fang, Jia Gu, Hamido Fujita, Jenq-Neng Hwang, “Depth Estimation Using A Self-Supervised Network based on Cross-layer Feature Fusion and the Quadtree Constraint,” IEEE T-CSVT, 32(4):1751-1766, April 2022
    • Yiling Xu, Qi Yang, Le Yang, Jenq-Neng Hwang, “EPES: Point Cloud Quality Modeling Using Elastic Potential Energy Similarity,” IEEE Trans. on Broadcasting, 68(1): 33-42, March 2022
    • C Zheng, J Zhang, JN Hwang, B Huang, “Double-Branch Dehazing Network based on Self-Calibrated Attentional Convolution,” Knowledge-Based Systems, Elsevier, Vol. 240, March 2022
    • W. Zhou, J. Jin, J. Lei, and J.-N. Hwang, “CEGFNet: Common Extraction and Gate Fusion Network for Scene Parsing of Remote Sensing Images,” IEEE Trans. on GeoScience and Remote Sensing, 60:1-10, Jan. 2022
    • Gaoang Wang, Yizhou Wang, Renshu Gu, Weijie Hu, Jenq-Neng Hwang, “Split and Connect: A Universal Tracklet Booster for Multi-Object Tracking,” IEEE Trans. on Multimedia, Jan. 2022 (early access)
    • Li Chen, et. al, “Domain Adaptive and Fully Automated Carotid Artery Atherosclerotic Lesion Detection using an Artificial Intelligence Approach (LATTE) on 3D MRI,” Magnetic Resonance in Medicine, 86(3):1662-1673, September 2021.
    • W. Zhou, X. Lin, J. Lei, L. Yu and J.-N. Hwang, “MFFENet: Multiscale Feature Fusion and Enhancement Network for RGB–Thermal Urban Road Scene Parsing,” IEEE Transactions on Multimedia, June 2021 (early access)
    • Yizhou~Wang, Zhongyu~Jiang,Yudong~Li, Jenq-Neng~Hwang, Guanbin~Xing, Hui~Liu,”RODNet: A Real-Time Radar Object Detection Network Cross-Supervised by Camera-Radar Fused Object 3D Localization,” IEEE Journal of Selected Topics in Signal Processing, special issue on Recent Advances in Automotive Radar Signal Processing, 15(4):954-967, June 2021.
    • Hung-Min Hsu, Jiarui Cai, Yizhou Wang, Jenq-Neng Hwang, Kwangju Kim, “Multi-Target Multi-Camera Tracking of Vehicles using Metadata-Aided Re-ID and Trajectory-Based Camera Link Model,” IEEE Trans. on Image Processing, 30:5198-5210, May 2021.
    • Kaiyig Zhu, XiaoyanJ iang, Zhijun Fang, Yongbin Gao, Hamido Fujita, Jenq-Neng Hwang, “Photometric transfer for direct visual odometry,” Knowledge-Based Systems, Volume 213, 15 February 2021