Publikationer pr. år
Publikationer pr. år
Personlig profil
Kort præsentation
My research field includes machine learning, pattern recognition, optimization methods and image analysis. I am particularly interested in applying deep learning methods for medical image analysis. My current ongoing project aims at developing a fully automated system which analyzes mammogram and predicts the risk of patient getting breast cancer. My major focus is developing efficient learning method which incorporates both supervised and unsupervised methods to train large scale deep neural networks.
Ekspertise relateret til FN’s Verdensmål
I 2015 blev FN-landende enige om 17 Verdensmål til at standse fattigdom, beskytte planeten og sikre velstand for alle. Denne persons arbejde bidrager til følgende verdensmål:
Fingeraftryk
Dyk ned i forskningsemnerne, hvor Pengfei Diao er aktive. Disse emneetiketter kommer fra dennes persons arbejder. Sammen danner de et unikt fingerprint.
- 10 Lignende profiler
Publikation
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Risk stratification of women with false-positive test results in mammography screening based on mammographic morphology and density: a case control study
Winkel, R. R., von Euler-Chelpin, M. C., Lynge, E., Diao, P., Lillholm, M., Kallenberg, M., Forman, J. L., Nielsen, M. B., Uldall, W. Y., Nielsen, M. & Vejborg, I. M. M., aug. 2017, I: Cancer Epidemiology. 49, s. 53-60 8 s.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review
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Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring
Kallenberg, M. G. J., Petersen, P. K., Nielsen, M., Ng, A. Y., Diao, P., Igel, C., Vachon, C. M., Holland, K., Winkel, R. R., Karssemeijer, N. & Lillholm, M., maj 2016, I: IEEE Transactions on Medical Imaging. 35, 5, s. 1322-1331 10 s.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review
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Assessing breast cancer masking risk in full field digital mammography with automated texture analysis
Kallenberg, M. G. J., Lillholm, M., Diao, P., Holland, K., Karssemeijer, N., Igel, C. & Nielsen, M., 2015, 7th International Workshop on Breast Densitometry and Cancer Risk Assessment (Non-CME). University of California, s. 109 1 s.Publikation: Bidrag til bog/antologi/rapport › Konferenceabstrakt i proceedings › Forskning › peer review
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Assessing breast cancer masking risk with automated texture analysis in full field digital mammography
Kallenberg, M. G. J., Lillholm, M., Diao, P., Petersen, K., Holland, K., Karssemeijer, N., Igel, C. & Nielsen, M., 2015, Breast Imaging and Interventional. Radiological Society of North America, Inc, s. 218 1 s.Publikation: Bidrag til bog/antologi/rapport › Konferenceabstrakt i proceedings › Forskning › peer review
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Automated texture scoring for assessing breast cancer masking risk in full field digital mammography
Kallenberg, M. G. J., Petersen, P. K., Lillholm, M., Jørgensen, D. R., Diao, P., Holland, K., Karssemeijer, N., Igel, C. & Nielsen, M., 2015, I: Insights into Imaging. 6, 1, Supplement, 1 s., B-0212.Publikation: Bidrag til tidsskrift › Konferenceabstrakt i tidsskrift › Forskning › peer review
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Inter-observer agreement according to three methods of evaluating mammographic density and parenchymal pattern in a case control study: impact on relative risk of breast cancer
Winkel, R. R., von Euler-Chelpin, M. C., Nielsen, M., Diao, P., Nielsen, M. B., Uldall, W. Y. & Vejborg, I. M. M., 12 apr. 2015, I: BMC Cancer. 15, 14 s., 274.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review
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Breast tissue segmentation and mammographic risk scoring using deep learning
Petersen, P. K., Nielsen, M., Diao, P., Karssemeijer, N. & Lillholm, M., 2014, Breast imaging: 12th International Workshop, IWDM 2014, Gifu City, Japan, June 29 – July 2, 2014. Proceedings. Fujita, H., Hara, T. & Muramatsu, C. (red.). Springer Science+Business Media, s. 88-94 7 s. (Lecture notes in computer science, Bind 8539).Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review