These are the sources and citations used to research Mike 5-15-2018. This bibliography was generated on Cite This For Me on

  • Journal

    Amato, F., López, A., Peña-Méndez, E. M., Vaňhara, P., Hampl, A. and Havel, J.

    Artificial neural networks in medical diagnosis

    2013 - Journal of Applied Biomedicine

    In-text: (Amato et al., 2013)

    Your Bibliography: Amato, F., López, A., Peña-Méndez, E., Vaňhara, P., Hampl, A. and Havel, J., 2013. Artificial neural networks in medical diagnosis. Journal of Applied Biomedicine, 11(2), pp.47-58.

  • Journal

    Andrysiak, T.

    Machine Learning Techniques Applied to Data Analysis and Anomaly Detection in ECG Signals

    2016 - Applied Artificial Intelligence

    In-text: (Andrysiak, 2016)

    Your Bibliography: Andrysiak, T., 2016. Machine Learning Techniques Applied to Data Analysis and Anomaly Detection in ECG Signals. Applied Artificial Intelligence, 30(6), pp.610-634.

  • Journal

    Arboleda, J., Aedo, J. and Rivera, F.

    Wireless system for supporting home health care of chronic disease patients

    2016 - 2016 IEEE Colombian Conference on Communications and Computing (COLCOM)

    In-text: (Arboleda, Aedo and Rivera, 2016)

    Your Bibliography: Arboleda, J., Aedo, J. and Rivera, F., 2016. Wireless system for supporting home health care of chronic disease patients. 2016 IEEE Colombian Conference on Communications and Computing (COLCOM),.

  • Journal

    Bychkov, D., Linder, N., Turkki, R., Nordling, S., Kovanen, P. E., Verrill, C., Walliander, M., Lundin, M., Haglund, C. and Lundin, J.

    Deep learning based tissue analysis predicts outcome in colorectal cancer

    2018 - Scientific Reports

    In-text: (Bychkov et al., 2018)

    Your Bibliography: Bychkov, D., Linder, N., Turkki, R., Nordling, S., Kovanen, P., Verrill, C., Walliander, M., Lundin, M., Haglund, C. and Lundin, J., 2018. Deep learning based tissue analysis predicts outcome in colorectal cancer. Scientific Reports, 8(1).

  • Journal

    Cabitza, F., Rasoini, R. and Gensini, G. F.

    Unintended Consequences of Machine Learning in Medicine

    2017 - JAMA

    In-text: (Cabitza, Rasoini and Gensini, 2017)

    Your Bibliography: Cabitza, F., Rasoini, R. and Gensini, G., 2017. Unintended Consequences of Machine Learning in Medicine. JAMA, 318(6), p.517.

  • Journal

    Guo, H., Chen, L., Chen, G. and Lv, M.

    Smartphone-based activity recognition independent of device orientation and placement

    2015 - International Journal of Communication Systems

    In-text: (Guo, Chen, Chen and Lv, 2015)

    Your Bibliography: Guo, H., Chen, L., Chen, G. and Lv, M., 2015. Smartphone-based activity recognition independent of device orientation and placement. International Journal of Communication Systems, 29(16), pp.2403-2415.

  • Journal

    Hassanpour, S., Korbar, B., Olofson, A., Miraflor, A., Nicka, C., Suriawinata, M., Torresani, L. and Suriawinata, A.

    Deep learning for classification of colorectal polyps on whole-slide images

    2017 - Journal of Pathology Informatics

    In-text: (Hassanpour et al., 2017)

    Your Bibliography: Hassanpour, S., Korbar, B., Olofson, A., Miraflor, A., Nicka, C., Suriawinata, M., Torresani, L. and Suriawinata, A., 2017. Deep learning for classification of colorectal polyps on whole-slide images. Journal of Pathology Informatics, 8(1), p.30.

  • Journal

    Heng, X., Wang, Z. and Wang, J.

    Human activity recognition based on transformed accelerometer data from a mobile phone

    2014 - International Journal of Communication Systems

    In-text: (Heng, Wang and Wang, 2014)

    Your Bibliography: Heng, X., Wang, Z. and Wang, J., 2014. Human activity recognition based on transformed accelerometer data from a mobile phone. International Journal of Communication Systems, 29(13), pp.1981-1991.

  • Journal

    Huang, C., Huang, K. and Jong, G.

    Artificial neural network integrated heart rate variability with detection system

    2013 - 2013 International Joint Conference on Awareness Science and Technology & Ubi-Media Computing (iCAST 2013 & UMEDIA 2013)

    In-text: (Huang, Huang and Jong, 2013)

    Your Bibliography: Huang, C., Huang, K. and Jong, G., 2013. Artificial neural network integrated heart rate variability with detection system. 2013 International Joint Conference on Awareness Science and Technology & Ubi-Media Computing (iCAST 2013 & UMEDIA 2013),.

  • Journal

    Janowczyk, A. and Madabhushi, A.

    Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases

    2016 - Journal of Pathology Informatics

    In-text: (Janowczyk and Madabhushi, 2016)

    Your Bibliography: Janowczyk, A. and Madabhushi, A., 2016. Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases. Journal of Pathology Informatics, 7(1), p.29.

  • Journal

    Lisboa, P.

    A review of evidence of health benefit from artificial neural networks in medical intervention

    2002 - Neural Networks

    In-text: (Lisboa, 2002)

    Your Bibliography: Lisboa, P., 2002. A review of evidence of health benefit from artificial neural networks in medical intervention. Neural Networks, 15(1), pp.11-39.

  • Journal

    Matta, S. C., Sankari, Z. and Rihana, S.

    Heart rate variability analysis using neural network models for automatic detection of lifestyle activities

    2018 - Biomedical Signal Processing and Control

    In-text: (Matta, Sankari and Rihana, 2018)

    Your Bibliography: Matta, S., Sankari, Z. and Rihana, S., 2018. Heart rate variability analysis using neural network models for automatic detection of lifestyle activities. Biomedical Signal Processing and Control, 42, pp.145-157.

  • Journal

    P.T., A. S., Joseph, P. K. and Jacob, J.

    Automated Diagnosis of Diabetes Using Heart Rate Variability Signals

    2011 - Journal of Medical Systems

    In-text: (P.T., Joseph and Jacob, 2011)

    Your Bibliography: P.T., A., Joseph, P. and Jacob, J., 2011. Automated Diagnosis of Diabetes Using Heart Rate Variability Signals. Journal of Medical Systems, 36(3), pp.1935-1941.

  • Journal

    Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., Sundberg, P., Yee, H., Zhang, K., Zhang, Y., Flores, G., Duggan, G. E., Irvine, J., Le, Q., Litsch, K., Mossin, A., Tansuwan, J., Wang, D., Wexler, J., Wilson, J., Ludwig, D., Volchenboum, S. L., Chou, K., Pearson, M., Madabushi, S., Shah, N. H., Butte, A. J., Howell, M. D., Cui, C., Corrado, G. S. and Dean, J.

    Scalable and accurate deep learning with electronic health records

    2018 - npj Digital Medicine

    In-text: (Rajkomar et al., 2018)

    Your Bibliography: Rajkomar, A., Oren, E., Chen, K., Dai, A., Hajaj, N., Hardt, M., Liu, P., Liu, X., Marcus, J., Sun, M., Sundberg, P., Yee, H., Zhang, K., Zhang, Y., Flores, G., Duggan, G., Irvine, J., Le, Q., Litsch, K., Mossin, A., Tansuwan, J., Wang, D., Wexler, J., Wilson, J., Ludwig, D., Volchenboum, S., Chou, K., Pearson, M., Madabushi, S., Shah, N., Butte, A., Howell, M., Cui, C., Corrado, G. and Dean, J., 2018. Scalable and accurate deep learning with electronic health records. npj Digital Medicine, 1(1).

  • Journal

    Silipo, R. and Marchesi, C.

    Artificial neural networks for automatic ECG analysis

    1998 - IEEE Transactions on Signal Processing

    In-text: (Silipo and Marchesi, 1998)

    Your Bibliography: Silipo, R. and Marchesi, C., 1998. Artificial neural networks for automatic ECG analysis. IEEE Transactions on Signal Processing, 46(5), pp.1417-1425.

  • Journal

    Trebeschi, S., van Griethuysen, J. J. M., Lambregts, D. M. J., Lahaye, M. J., Parmar, C., Bakers, F. C. H., Peters, N. H. G. M., Beets-Tan, R. G. H. and Aerts, H. J. W. L.

    Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric MR

    2017 - Scientific Reports

    In-text: (Trebeschi et al., 2017)

    Your Bibliography: Trebeschi, S., van Griethuysen, J., Lambregts, D., Lahaye, M., Parmar, C., Bakers, F., Peters, N., Beets-Tan, R. and Aerts, H., 2017. Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric MR. Scientific Reports, 7(1).

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