- 2017: Master of Science in Biomedical Computing. Technical University of Munich, Germany
Thesis: Webly Supervised Learning for Skin Lesion Classification
- 2015: Bachelor in Mechatronics Engineering. University of Guanajuato, Mexico
Thesis: Natural Image Segmentation Using Rough-Set-Based-Rules
- Medical image analysis: algorithms for medical applications.
- Machine Learning: Weak and and unsupervised learning
- Deep Learning in Medical Imaging: classification, segmentation, localization for computer-aided systems.
Navarro, F., Conjeti, S., Tombari, F. and Navab, N.
(2018, March). Webly Supervised Learning for Skin Lesion Classification. arXiv preprint arXiv:1804.00177.
Paschali, M., Conjeti, S., Navarro, F. and Navab, N.
(2018, March). Generalizability vs. Robustness: Adversarial Examples for Medical Imaging. arXiv preprint arXiv:1804.00504.
Navarro, F., Saint-Hill-Febles, Y., Renner, J., Klare, P., von Delius, S., Navab,
N., & Mateus, D.
(2017, March). Computer assisted optical biopsy for colorectal
polyps. In SPIE Medical Imaging (pp. 101340J-101340J). International Society
for Optics and Photonics.
Navarro-Avila, F. J., Cepeda-Negrete, J., & Sanchez-Yanez, R. E.
(2016, June). Toward the Labeled Segmentation of Natural Images Using Rough-Set Rules. In Mexican Conference on Pattern Recognition (pp. 74-83). Springer International Publishing.
| UsersForm |
| Title: || M.Sc. |
| Circumference of your head (in cm): || 200 |
| Firstname: || Fernando |
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| Lastname: || Navarro |
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| Birthday: || 14.01.1991 |
| Nationality: || Mexico |
| Languages: || English, German, Spanish |
| Groups: || |
| Expertise: || Medical Imaging |
| Position: || Scientific Staff |
| Status: || Active |
| Emailbefore: || fernando.navarro |
| Emailafter: || tum.de |
| Room: || IMETUM/TranslaTUM |
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