Welcome to the Medical Image and Health Informatics Lab led by Prof. Xiaohua Qian in the School of Biomedical Engineering at Shanghai Jiao Tong University, China. Our lab is dedicated to an interdisciplinary research programme that develops advanced artificial intelligence algorithms and informatics systems to address clinical challenges for health care. Research focuses on medical image (video) processing and machine learning, including classification and prediction, detection and segmentation, and health big data mining. The main technical challenges we aim to address are small sample and fine-grained analysis, stability and generalization of models, and high-dimensional data mining.

Recent News more

Video-based intelligent assessment of Parkinson’s disease

Parkinson’s disease (PD) is a serious neurodegenerative disease, and is faced with the realistic problems of few doctors, nonobjective diagnosis and discontinuous follow-ups. Currently, the Movement Disorder Society‐sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is the gold standard for clinical diagnosis and evaluation of PD, and hence intelligent assessment based on this scale is very crucial.
MedIA 2022 · IEEE TMM 2022 · IEEE TCSVT 2022 · IEEE TNSRE 2021

Medical imaging informatics system for early diagnosis and treatment of pancreatic cancer

Pancreatic cancer is one of the most lethal malignant tumors and is considered as the "king of cancers" for the following possible reasons: 1) lack of early alert indications and effective screening/early diagnosis instruments, resulting in most patients being diagnosed at the advanced stage; 2) poor prognostic due to difficulty in preoperative assessment of prognostic risk factors; 3) surgical resection is the most effective treatment, but it is regarded as the most challenging general surgery due to its complicated anatomical structure and the involvement of critical blood vessels.
IEEE TMI 2023 · MedIA 2023 · IEEE JBHI 2023 · MedIA 2022 · IEEE TMI 2022 · IEEE JBHI 2022 · NEUROCOMPUTING 2021 · Phys. Med. Biol. 2021

Biomedical data mining

Biomedical data analysis faces technical challenges such as missing values, small sample sizes, and high dimensions. Specific clinical demands often make the collection of clinical data only partially available, resulting in missing values. This also leads to a small sample size; that is, the samples containing the same data modal are always few. In addition, a certain modal of data may suffer from a high dimensionality.