Page 96 - ITU KALEIDOSCOPE, ATLANTA 2019
P. 96

2019 ITU Kaleidoscope Academic Conference




                 (2019). Benchmarking Robustness in Object     [70]   Bender, D., & Sartipi, K. (2013). HL7 FHIR: An
                 Detection: Autonomous Driving when Winter is       Agile and RESTful approach to healthcare
                 Coming. arXiv preprint.                            information exchange. In Proceedings of the 26th
                 https://arxiv.org/abs/1907.07484                   IEEE international symposium on computer-based
                                                                    medical systems (pp. 326-331). IEEE.
           [63]  Filos, A., Farquhar, S., Gomez, A. N., Rudner, T. G.   https://doi.org/10.1109/CBMS.2013.6627810
                 J.. Kenton, Z., Smith, L., Alizadeh, M., de Kroon, A.
                 & Gal, Y (2019). Benchmarking Bayesian Deep   [71]   Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J.,
                 Learning with Diabetic Retinopathy Diagnosis.      Appleton, G., Axton, M., Baak, A., ... & Bouwman,
                 Preprint. Retrieved from                           J. (2016). The FAIR Guiding Principles for scientific
                 http://www.cs.ox.ac.uk/people/angelos.filos/publicat  data management and stewardship. Scientific data, 3.
                 ions/diabetic_retinopathy_diagnosis.pdf            https://doi.org/10.1038/sdata.2016.18

           [64]   Parikh, R. B., Obermeyer, Z., & Navathe, A. S.    [72]  Blum, A., & Hardt, M. (2015). The ladder: A
                 (2019). Regulation of predictive analytics in      reliable leaderboard for machine learning
                 medicine. Science, 363(6429), 810-812.             competitions. arXiv preprint.
                 https://doi.org/10.1126/science.aaw0029            https://arxiv.org/abs/1502.04585

           [65]  Finlayson, S. G., Bowers, J. D., Ito, J., Zittrain, J. L.,   [73]  Anderson-Cook, C. M., Myers, K. L., Lu, L.,
                 Beam, A. L., & Kohane, I. S. (2019). Adversarial   Fugate, M. L., Quinlan, K. R., & Pawley, N. (2019).
                 attacks on medical machine learning. Science,      How to Host a Data Competition: Statistical Advice
                 363(6433), 1287-1289.                              for Design and Analysis of a Data Competition.
                 https://doi.org/10.1126/science.aaw4399            arXiv preprint. https://arxiv.org/abs/1901.05356


           [66]   Lapuschkin, S., Wäldchen, S., Binder, A.,   [74]  Binder, A., Bockmayr, M., Hägele, M., Wienert, S.,
                 Montavon, G., Samek, W., & Müller, K. R. (2019).   Heim, D., Hellweg, K., ... & Treue, D. (2018).
                 Unmasking Clever Hans predictors and assessing     Towards computational fluorescence microscopy:
                 what machines really learn. Nature communications,   Machine learning-based integrated prediction of
                 10(1), 1096. https://doi.org/10.1038/s41467-019-   morphological and molecular tumor profiles. arXiv
                 08987-4                                            preprint. https://arxiv.org/abs/1805.11178

           [67]  Voosen, P. (2017). The AI detectives. Science,   [75]  Klauschen, F., Müller, K. R., Binder, A., Bockmayr,
                 357(6346), pp. 22-27.                              M., Hägele, M., Seegerer, P., ... & Michiels, S.
                 https://doi.org/10.1126/science.357.6346.22        (2018, October). Scoring of tumor-infiltrating
                                                                    lymphocytes: From visual estimation to machine
           [68]   Gebru, T., Morgenstern, J., Vecchione, B., Vaughan,   learning. In Seminars in cancer biology (Vol. 52, pp.
                 J. W., Wallach, H., Daumeé III, H., & Crawford, K.   151-157). Academic Press.
                 (2018). Datasheets for datasets. arXiv preprint.   https://doi.org/10.1016/j.semcancer.2018.07.001
                 https://arxiv.org/abs/1803.09010

           [69]   Mildenberger, P., Eichelberg, M., & Martin, E.
                 (2002). Introduction to the DICOM standard.
                 European radiology, 12(4), 920-927.
                 https://doi.org/10.1007/s003300101100



























                                                           – 76 –
   91   92   93   94   95   96   97   98   99   100   101