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M. Kandemir, “Asymmetric transfer learning with deep Gaussian processes”, ICML. Proceedings. pp. 730-738, 2015.PDF icon Technical Report (570.95 KB)
M. Kandemir, Feuchtinger, A., Walch, A., and Hamprecht, F. A., “Digital Pathology: Multiple instance learning can detect Barrett'scancer”, ISBI. Proceedings. pp. 1348-1351, 2014.PDF icon Technical Report (2.86 MB)
M. Kandemir and Hamprecht, F. A., “Cell event detection in phase-contrast microscopy sequences from few annotations”, MICCAI. Proceedings, vol. LNCS 9351. Springer, pp. 316-323, 2015.PDF icon Technical Report (564.69 KB)
M. Kandemir and Hamprecht, F. A., “Computer-aided diagnosis from weak supervision: A benchmarking study”, Computerized Medical Imaging and Graphics, vol. 42, pp. 44-50, 2014.PDF icon Technical Report (4.28 MB)
M. Kandemir and Hamprecht, F. A., “Instance Label Prediction by Dirichlet Process Multiple Instance Learning”, in UAI. Proceedings, 2014.PDF icon Technical Report (4.26 MB)
M. Kandemir, Klami, A., Gonen, M., Vetek, A., and Kaski, S., “Multi-task and multi-view learning of user state”, Neurocomputing, vol. 139, pp. 97-106, 2014.
M. Kandemir, Rubio, J. C., Schmidt, U., Welbl, J., Ommer, B., and Hamprecht, F. A., “Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures”, in MICCAI. Proceedings, 2014, pp. 154-161.PDF icon Paper (2 MB)
M. Kandemir, Zhang, C., and Hamprecht, F. A., “Empowering multiple instance histopathology cancer diagnosis by cell graphs”, in MICCAI. Proceedings, 2014, vol. 8674, pp. 228-235.PDF icon Technical Report (1.76 MB)
M. Kandemir, Hamprecht, F. A., Wojek, C., and Schmidt, U., “Active machine learning for training an event classification”, Patent, Patent Number WO2017032775 A1, 2017.
M. Kandemir, Hamprecht, F. A., Wojek, C., and Schmidt, U., “Maschinelles Lernen”, Patent, Patent Number WO2017032775A1, 2017.PDF icon Technical Report (317.04 KB)