Medical Image Processing

Multi-modal, multi-resolution visualization
of structure and functions of human organs

AI in Healthcare

o AI in Medical Imaging
o Deep Learning in Diagnosis and Therapy Assistance
o Multi-modal Medical Data Fusion

Big data in Healthcare

o Genomic Data Management and Analysis
o Big Data in Cognitive Computing
o Data-Driven Disease Prediction and Prevension


Intelligent Medical Devices

o Healthcare IOT
o Wearable Medical Devices
o Intelligent Clinical Assist Systems
o Medical Devices Validation and Certification



o Single-Cell Data Analysis
o Deep Learning in Genetics
o Drug Target Filtering for Cancer


Center Introduction

SMIRC at ShanghaiTech University focuses on application of information technologies in medicine and healthcare. The mission of our center is to develop future healthcare technologies that are intelligent, affordable, precise. Our research focus on theoretical research and clinical application of disease mechanisms, biological signal processing, intelligent diagnosis, physiological modeling and medical device safety. Our center consists of more than 10 full-time faculty members and affliated researchers. Our research area include medical image processing, AI in healthcare, big data in healthcare, intelligent medical devices and bio-informatics. Our center promotes ShanghaiTech's international influence in biomedical engineeringhas by establishing comprehensive collaborations with hospitals and medical industries and educating researchers with inter-disciplinary vision.

Research Areas

SMIRC has the following 5 research areas:

Medical Imaging

Medical imaging is the technique and process to visualize the internal organs, structures and functions for human body. It provides efficient support for clinical diagnosis and treatment. It is one of the most important fields of modern medical research and application. Medical imaging is a comprehensive discipline that closely combines Life science, Medicine, Physics, Engineering, and Computer Science. With the rapid development in Computer Science, such as artificial intelligence, and big data; as well as various advanced medical imaging technique and equipment, modern medical imaging has also entered a fast developing pathway. Combining the advanced computer science with medical imaging to make conventional technology more intelligent, accurate and reliable, will further improve the importance of medical imaging technology in disease diagnosis, prediction and surgical treatment. Take advantage of big data and artificial intelligence, to achieve more precise medical treatment and telemedicine is an important direction for the development of medical imaging in the future.

AI in Healthcare

The purpose of medical artificial intelligence is to develop and exploit computer algorithms to approximate the cognitive process of clinicians in analyzing complex medical data, so that relevant medical conclusions can be automatically drawn. This can assist doctors in analyzing the relevance and effects of preventive or diagnostic techniques and treatment of diseases. Specifically, medical AI uses state-of-the-art algorithms to learn effective features from a large amount of healthcare data (including the latest medical information from journals, textbooks, and clinical practice), and the acquired knowledge is then used to assist clinical practice, helping to reduce the inevitable diagnosis and treatment errors, as well as real-time inference and prediction of health risk alerts and health results. With the recent rapid growth of medical data and the remarkable development of machine (deep) learning methods, artificial intelligence has achieved significant success in the healthcare community. Artificial intelligence programs will be widely used in the diagnosis procedure, treatment options, drug discovery, personalized medicine and patient monitoring and nursing practices. The center has the following topics in smart medicine:
  • Artificial intelligence assisted medical imaging
  • Adjuvant diagnosis and treatment based on deep learning
  • Multimodal medical data fusion

Big Data in Healthcare

With the rapid development of biomedical and information technologies, healthcare big data has drawn more and more attention around the world, as a kind of information resources with great potential value. In particular, the artificial intelligence (AI) technologies, which are under intensive innovation and advancement in recent years, are expected to transform the data into great economic and social values (e.g. to improve medical and healthcare quality). However, the healthcare big data has also posed challenges of information technology, such as how to store and transmit massive amounts of data, how to analyze unstructured data, insufficient manpower or training of informatic skills among doctors, nurses or patients, etc. Our research goals include addressing technical challenges in healthcare big data (e.g. data management and analytics, decision-support), using AI and cognitive computing to automate the analysis of healthcare big data, using the big data to optimize the precision, efficiency and quality of clinical diagnosis and treatment, etc. Such technologies can improve the utility of the data resources, and make it easier for medical professionals and researchers to analyze their data. Our research in healthcare big data includes the following directions:
  • Genomics data management and analytics
  • Cognitive computing for healthcare big data
  • Data-driven diagnosis and prevention of diseases

Intelligent Medical Devices

With the development of technologies, medical devices are becoming "smarter" such that 1) they are autonomously making safety-critical decisions on diagnosis and therapy, and 2) they are coorperating by interconnections. The autonomy allows timely therapy and better life quality, and the connectivity allows additional perspectives for more precise diagnosis and therapy. However, these complex features are achieved by increasingly complex software, which puts the patients at higher risks when device malfunctions occur, and poses great challenges on traditional medical device development, validation and certification.

SMIRC has the following research projects under Intelligent Medical Devices:

  • Medical IOT
  • Wearable Medical Devices
  • Intelligent Clinical Support Systems
  • Medical Devices Software Validation and Certification


Since the Human Genome Project was completed, there has been rapid advancement in biotechnologies, such as the next-generation sequencing (NGS). As a result, the emerging of large amounts of molecular biology and multi-omics data calls for cutting-edge information technologies (e.g. data science and machine learning algorithms) to process, store, manage and analyze the data. Our research goals include the development of new computational methodologies (including algorithms, database, statistics and modeling, etc.) to analyze the data more efficiently and accurately, to find patterns in the data, and to accelerate biological discoveries using information technology. For instance, we use the state-of-the-art computational methods to explain biological phenomena and predict behaviors of biological systems (e.g. cell fate decision), and provide computational support for wet-lab biological research. Bioinformatics is an inter-disciplinary field, involving knowledge and techniques from multiple disciplines, including but not limited to: algorithms, statistics, machine learning, artificial intelligence, database and data mining, dynamical systems theory, population genetics, etc. The technologies can be applied to many life science-related fields, especially disease studies, drug design, agriculture, ageing and longevity research, etc. Our bioinformatics research includes the following directions:
  • Single-cell data analysis
  • Deep learning in genomics
  • Virtual stem cells
  • Computational screening of anticancer drug targets