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| Doan M, Sebastian JA, Caicedo JC, et al. (2020) Objective assessment of stored blood quality by deep learning. Proceedings of the National Academy of Sciences of the United States of America |
| Doan M, Case M, Masic D, et al. (2020) Label-Free Leukemia Monitoring by Computer Vision. Cytometry. Part a : the Journal of the International Society For Analytical Cytology |
| Nassar M, Doan M, Filby A, et al. (2019) Label-Free Identification of White Blood Cells Using Machine Learning. Cytometry. Part a : the Journal of the International Society For Analytical Cytology |
| Doan M, Vorobjev I, Rees P, et al. (2018) Diagnostic Potential of Imaging Flow Cytometry. Trends in Biotechnology |
| Caicedo JC, Cooper S, Heigwer F, et al. (2017) Data-analysis strategies for image-based cell profiling. Nature Methods. 14: 849-863 |
| Doan M, Case M, Mašić D, et al. (2017) Label-Free Analyses of Minimal Residual Disease in ALL Using Deep Learning and Imaging Flow Cytometry Blood. 130: 1437-1437 |
| Piasecka J, Hennig H, Theis FJ, et al. (2017) Label Free Identification of Peripheral Blood Eosinophils Using High-Throughput Imaging Flow Cytometry Journal of Allergy and Clinical Immunology. 139: AB163 |
| Hennig H, Rees P, Blasi T, et al. (2016) An open-source solution for advanced imaging flow cytometry data analysis using machine learning. Methods (San Diego, Calif.) |
| Blasi T, Hennig H, Summers HD, et al. (2016) Label-free cell cycle analysis for high-throughput imaging flow cytometry. Nature Communications. 7: 10256 |
| Jeong YM, Duanting Z, Hennig H, et al. (2015) CDy6, a photostable probe for long-term real-time visualization of mitosis and proliferating cells. Chemistry & Biology. 22: 299-307 |