Biomedical Artificial Intelligence
Machine learning algorithms, data mining methods, intelligent diagnosis methods of complex diseases, intelligent drug discovery / design methods, intelligent biosynthesis methods, etc
ECG Cloud Diagnosis
Based on cloud ECG diagnostic platform, distributed HDFS data center, deep learning model and machine
learning diagnosis
Doctor-assisted ECG diagnostic platform, micro-service system architecture
ECG Cloud Center
Cloud-based doctor service diagnosis platform
HDFS Data Center
Distributed data storage center
Deep learning model diagnosis
Multi-class primary screening auxiliary diagnosis
Breast Cancer Diagnosis
All-round assisted pathologists complete breast cancer related diagnosis and treatment quickly and accurately
Automated diagnosis of HE image in breast cancer
Computer modeling to learn a variety of pathological species characteristics and to identify the disease types of HE pathological sections
Invasive breast cancer histological grading
Simulated doctor diagnosis, the computer is identified by three aspects: glandular duct, nucleus, and mitosis.
Automated analysis of immunohistochemical HER2 test results
Automated analysis of HER2 results through a deep learning network
Fluorescence in situ hybridization HER2 gene status detection
Automatically count the number of HER2 and CEP17 according to the HER2 guide to assist in the detection of HER2 gene status
Cervical cancer diagnosis
The computer automatically recognizes abnormal cervical cells, providing pathologists with high-efficiency,
high-accuracy auxiliary diagnosis
Abnormal cervical cell detection
Segmentation of cervical cells, combined with pathological knowledge to extract morphological and texture features of cells, and automatic classification of cervical cells based on machine learning techniques
Abnormal cervical cell detection
A single cervical cell image is generated by nuclear automatic localization technology, and then combined with a deep convolutional neural network model to automatically detect abnormal cervical cells.
Abnormal cervical tissue biopsy
Multi-scale analysis of cervical tissue section cell morphology, automatic location of suspicious lesions, and automatic identification of abnormal tissue section areas by target detection techniques
Drug-disease association prediction
Based on existing multi-omics data on drugs, proteins, disease phenotypes, genes, side effects, pathways,
etc.
Develop appropriate machine learning algorithms to mine potential drug-disease associations,
Provide reference for drug repositioning (new use of old drugs) and new drug discovery
Heterogeneous Network
Machine Learning
Prediction
Laboratory public mailbox
biodwhu@163.com
Wuchang District, Wuhan City, Hubei Province
Wuhan University School
of Computer ScienceB503