Area of research
Artificial Intelligence · Radiology, Nuclear Medicine and Imaging
Research interest
Research interests include AI in cancer detection, Radiomics and Machine Learning in Medical Imaging, Breast Cancer Treatment Studies, and Cancer Immunotherapy and Biomarkers.
HER2 testing in multifocal/multicentric breast cancer: should all foci be tested in the context of HER2-low and HER2-ultralow?
Artificial intelligence enhances whole‐slide interpretation of PD‐L1 CPS in triple‐negative breast cancer: A multi‐institutional ring study
Tumour mutation burden and infiltrating immune cell subtypes influenced the breast cancer prognosis
Multi-center study on predicting breast cancer lymph node status from core needle biopsy specimens using multi-modal and multi-instance deep learning
High Dynamic Range Dual-Modal White Light Imaging Improves the Accuracy of Tumor Bed Sampling After Neoadjuvant Therapy for Breast Cancer
Comparison of the tumor immune microenvironment phenotypes in different breast cancers after neoadjuvant therapy
Reconstructing virtual large slides can improve the accuracy and consistency of tumor bed evaluation for breast cancer after neoadjuvant therapy
High dynamic range dual-modal white light imaging system improves the accuracy of tumor bed sampling after neoadjuvant therapy for breast cancer
Multi-center study on predicting breast cancer lymph node status from core needle biopsy specimens using multi-modal and multi-instance deep learning
Multi-modal Multi-instance Learning Using Weakly Correlated Histopathological Images and Tabular Clinical Information
Development and Validation of a Novel Model for Predicting Prognosis of Non-PCR Patients After Neoadjuvant Therapy for Breast Cancer
[AI-assisted Prediction of Lymph Node Metastasis of Breast Cancer: Current and Prospective Research].
Development and validation of a nomogram for predicting the status of estrogen receptor-low-positive breast cancer