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Fang‐I Lu

University of Toronto · CA
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Area of research
Cancer Research · Artificial Intelligence
Research interest
Research interests include Breast Cancer Treatment Studies, AI in cancer detection, Breast Lesions and Carcinomas, and Radiomics and Machine Learning in Medical Imaging.
h-index
21
citations
1,572
works
81
NIH funding
primary concept
email

Recent publications

Machine learning analysis of breast ultrasound to classify triple negative and HER2+ breast cancer subtypes
Breast Disease 2023cited by 25position: middledoi
Predicting Patterns of Distant Metastasis in Breast Cancer Patients following Local Regional Therapy Using Machine Learning
Genes 2023cited by 12position: middledoi
Quantitative digital histopathology and machine learning to predict pathological complete response to chemotherapy in breast cancer patients using pre-treatment tumor biopsies
Scientific Reports 2022cited by 46position: middledoi
A review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks
Scientific Reports 2021cited by 73position: middledoi
Machine Learning Frameworks to Predict Neoadjuvant Chemotherapy Response in Breast Cancer Using Clinical and Pathological Features
JCO Clinical Cancer Informatics 2021cited by 51position: middledoi
Analysis of tumor nuclear features using artificial intelligence to predict response to neoadjuvant chemotherapy in high-risk breast cancer patients
Breast Cancer Research and Treatment 2021cited by 40position: lastdoi
Assessment of Digital Pathology Imaging Biomarkers Associated with Breast Cancer Histologic Grade
Current Oncology 2021cited by 21position: middledoi
Computational Radiology in Breast Cancer Screening and Diagnosis Using Artificial Intelligence
Canadian Association of Radiologists Journal 2020cited by 79position: middledoi
Application of a risk-management framework for integration of stromal tumor-infiltrating lymphocytes in clinical trials
npj Breast Cancer 2020cited by 19position: middledoi
Personalized Breast Cancer Treatments Using Artificial Intelligence in Radiomics and Pathomics
Journal of medical imaging and radiation sciences 2019cited by 92position: middledoi

Grants

No grants ingested yet.

Frequent collaborators

Ali Sadeghi‐Naini · Sunnybrook Health Science Centre9 papers (2019–2023)William T. Tran · York University9 papers (2019–2023)Andrew Lagree · Sunnybrook Health Science Centre8 papers (2019–2023)Sonal Gandhi · University of Illinois Chicago5 papers (2020–2023)Ethan Law · Sunnybrook Health Science Centre5 papers (2019–2023)Khadijeh Saednia · York University5 papers (2019–2023)Sami Tabbarah · Sunnybrook Health Science Centre4 papers (2019–2023)Katarzyna J. Jerzak · Sunnybrook Health Science Centre4 papers (2019–2023)Lauren Fleshner · Sunnybrook Health Science Centre4 papers (2021–2023)Elzbieta Slodkowska · Sunnybrook Health Science Centre3 papers (2021–2021)David W. Dodington · Sunnybrook Health Science Centre3 papers (2021–2022)Nicholas Meti · McGill University Health Centre3 papers (2020–2021)Brianna Law · Sunnybrook Health Science Centre3 papers (2021–2023)Audrey Shiner · Sunnybrook Health Science Centre3 papers (2021–2023)Alex Shenfield · University of York3 papers (2021–2023)Marie Angeli Alera · Sunnybrook Health Science Centre3 papers (2021–2023)Eileen Rakovitch · Sunnybrook Health Science Centre2 papers (2020–2021)Jonathan Klein · Sunnybrook Health Science Centre2 papers (2019–2023)Belinda Curpen · Sunnybrook Health Science Centre2 papers (2020–2023)Majid Mohebpour · McGill University Health Centre2 papers (2021–2021)
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