Mr. Han Yaokun (B.Sc. in Computer Science, Year 2) from Department of Computer Science and Engineering has been awarded the Hong Kong Institution of Engineers (HKIE) Scholarship 2024-25. Mr. Han was honored at the HKIE Prize Presentation Ceremony cum New Members’ Reception held on 11 January 2025. 

The HKIE Scholarship scheme aims to recognize the academic achievements of outstanding full-time engineering undergraduate students. Each year, the scholarship is awarded to up to three students who major in HKIE-accredited engineering programs in any local university, and each successful candidate will receive a certificate and a total grant of HK$80,000 which will be disbursed to the awardees in four instalments upon the fulfilment of specific requirements. Selection criteria include academic results at university, enthusiasm in becoming an engineer, and leadership ability demonstrated in extra-curricular activities and community service.

Three research projects led by Professors from Faculty of Engineering have received grants of over HK$20 million from the Collaborative Research Fund (CRF) 2024/25 by the Research Grants Council (RGC). Among them, two projects have been awarded a Collaborative Research Project Grant, and one secured a Young Collaborative Research Grant.

Professor Mao Chuanbin from Department of Biomedical Engineering (Project title: Tumor-homing Immunotherapeutic Phages for Efficiently Treating Hepatocellular Carcinoma) and Professor Zhang Li from Department of Mechanical and Automation Engineering (Project title: Development of Modular Microrobots and the Image-guided Intervention for Minimally Invasive Anti‐adhesion Treatment after Tubal Cannulation) have been awarded the Collaborative Research Project Grant with grant of over HK$8 million for each project.

Professor Huang Chaoran from Department of Electronic Engineering (Project title: 3D Photonic-electronic Neural Network Enabling Versatile and Large-scale AI Computing) secured the Young Collaborative Research Grant with grant of over HK$4 million for her project.

About the Collaborative Research Fund (CRF)

The Collaborative Research Fund (CRF) supports multi-investigator, multi-disciplinary projects to encourage more research groups to engage in creative, high-quality cross-disciplinary/cross-institutional projects. There are three types of grants under CRF: 1) The Collaborative Research Project Grant encourages research groups in UGC-funded universities to collaborate across disciplines and across universities, enhancing research output. It funds staff, equipment and general expenses. The RGC emphasises capacity building and the potential for developing research strengths. 2) The Collaborative Research Equipment Grant enables the acquisition of major research facilities or equipment for collaborative research, assists universities in leveraging support from equipment suppliers, and provides funding for group user fees to access major facilities. 3) The Young Collaborative Research Grant supports early-stage academic staff in leading and managing collaborative research, preparing them for larger funding opportunities. Only group research proposals are eligible.

More details: CUHK receives over HK$69 million from RGC’s Collaborative Research Fund 2024/25

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The research team led by Professor Scott Yuan Wu, Assistant Professor of Department of Biomedical Engineering at The Chinese University of Hong Kong (CUHK), in collaboration with Professor Wang Ningli’s team at Beijing Tongren Hospital, has developed an artificial intelligence (AI) ophthalmic imaging foundation model called VisionFM. This model enables the prediction of the presence of tumors from retinal images for the first time, representing a breakthrough in ophthalmic disease diagnosis. The research foundings have been published in NEJM AI, a publication of the New England Journal of Medicine Group.

Vision impairment and its associated major eye diseases, such as cataracts, age-related macular degeneration and glaucoma, have become a pressing global health problem. It is estimated that by 2050, about 474 million people may suffer from moderate to severe vision impairment. The situation is particularly acute in low-income countries where there is a shortage of specialised ophthalmologists. Despite the rapid advancements of AI in automated ophthalmic diagnosis, there are still significant limitations to existing models. They often rely on vast amounts of labelled data, which is relatively time-consuming and costly to collect. In addition, many existing models target on a single or limited number of eye diseases and utilise only one imaging modality, such as fundus photographs, preventing their broader application in clinical diagnosis.

To address these challenges, the CUHK and Beijing Tongren Hospital research teams developed VisionFM, a groundbreaking AI ophthalmic imaging foundation model. VisionFM was pre-trained on the world’s largest ophthalmic data cohort containing 3.4 million images from eight different ophthalmic modalities, covering a wide range of ophthalmic diseases, imaging modalities and devices, as well as clinical scenarios. This innovative model has been tested for multiple applications, including ophthalmic disease diagnosis, progression prediction, systemic biomarker prediction through ocular imaging, intracranial tumor prediction, and lesion, vessel, layer segmentation.

VisionFM outperforms existing models in ophthalmic disease diagnosis, achieving diagnostic accuracy comparable to an ophthalmologist with 4-8 years of clinical experience. Additionally, VisionFM demonstrates remarkable few-shot learning capabilities in diagnosis and anatomical segmentation, enabling it to adapt to imaging modalities and devices not encountered in the pre-training phase, or require only a small  number of gold-standard samples for rapid fine-tuning. The model also reveals for the first time the association between intracranial tumors and retinal images, enabling the prediction of tumors directly from low-cost retinal images. This holds great potential for the early detection in community and primary care. The model has been deployed to diagnose common eye diseases in Henan Province.

Furthermore, the research result indicates that high-quality synthetic ophthalmic data, which passed the Turing test, can significantly enhance the pre-training of foundation models like VisionFM, further improving their efficacy. With its open-source codebase and model, it is poised to address the global challenges and improve patient outcomes through advanced AI technology.  

The full research paper can be found at: https://ai.nejm.org/doi/full/10.1056/AIoa2300221

About NEJM AI and the research article

About NEJM AI

NEJM AI is a new journal launched by the New England Journal of Medicine (NEJM) in 2024, focusing on groundbreaking research in artificial intelligence in the medical field. As the newest member of the most influential family of journals in the medical community, NEJM AI upholds NEJM's rigorous academic standards and is dedicated to publishing AI innovations that can truly transform medical practice. The launch of this journal marks the recognition of the importance of artificial intelligence in the field of medicine by the mainstream medical community.

Research Article

 “ Development and Validation of a Multimodal Multitask Vision Foundation Model for Generalist Ophthalmic Artificial Intelligence” by Professor Scott Yuan Wu’s team at CUHK in collaboration with Professor Wang Ningli’s team at Beijing Tongren Hospital

Authors：Qiu Jianing†, Wu Jian†, Wei Hao†, Shi Peilun, Zhang Minqing, Sun Yunyun, Li Lin, Liu Hanruo, Liu Hongyi, Hou Simeng, Zhao Yuyang, Shi Xuehui, Xian Junfang, Qu Xiaoxia, Zhu Sirui, Pan Lijie, Chen Xiaoniao, Zhang Xiaojia, Jiang Shuai, Wang Kebing, Yang Chenlong, Chen Mingqiang, Fan Sujie, Hu Jianhua, Lv Aiguo, Miao Hui, Guo Li, Zhang Shujun, Pei Cheng, Fan Xiaojuan, Lei Jianqin, Wei Ting, Duan Junguo, Liu Chun, Xia Xiaobo, Xiong Siqi, Li Junhong, Lam Kyle, Lo Benny, Tham Yih-chung, Wong Tien-yin, Wang Ningli ∗, Yuan Wu ∗

† Co-first；∗ Co-corresponding

Led by the Chief Secretary for Administration, the Strive and Rise Programme (「共 創明『Teen』計劃」) has been taken forward by an inter-departmental Task Force through tripartite collaboration between the Government, the business sector and the community.  The Strive and Rise Alumni Club (共創明 teen 校友會) provides a platform for graduating mentees to broaden their horizons through activities and internship programs continuously.

Organized by Tencent, being one of the supporting organization, CUHK Faculty of Engineering had been receiving over 80 secondary school students from the Strive and Rise Alumni Club on a programming event held on 27 December 2024 (Friday). This series of programme aim to provide opportunity for participating students to understand recent development of the technology industry and inspire their life planning through different elements such as technology learning, university life experience, and career enlightenment.

Two professors from The Chinese University of Hong Kong (CUHK)’s Faculty of Engineering have been elected Fellows of the prestigious Institute of Electrical and Electronics Engineers (IEEE) in the class of 2025. They are Professor CHAN Yuen Yan Rosanna from Department of Information Engineering and Professor PAN Sinno Jialin from Department of Computer Science and Engineering. 

Professor Chan has been elevated to IEEE Fellow for her contributions to learning technologies for special education needs and social inclusion. Professor Chan’s research focuses on integrating innovative technologies into educational practices, enhancing learning experiences for individuals with special needs. Her dedication to promoting social inclusion through technology has established her as a leader in the field, inspiring both educators and students alike.

Professor Pan was elected for his contributions to transfer learning methodologies. Professor Pan is a Professor and Global STEM Scholar with the Department of Computer Science and Engineering at the Chinese University of Hong Kong (CUHK) in Hong Kong. He is also the Director of the JC STEM Lab of Machine Learning and Symbolic Reasoning. Prof. Pan’s research interests focus on machine learning and artificial intelligence, especially transfer learning and its applications.

About IEEE

The IEEE is the world’s largest professional organisation dedicated to advancing technological innovation and excellence for the benefit of humanity, with more than 430,000 members in over 160 countries. IEEE Fellow is the highest grade of membership and is recognised by the technical community as a prestigious honour and an important career achievement.