The Role of Machine Learning in Medical Data Analysis. A Case Study: Flow Cytometry

Paolo Rota, Florian Kleber, Michael Reiter, Stefanie Groeneveld-Krentz, Martin Kampel

2016

Abstract

In last years automated medical data analysis turned out to be one of the frontiers of Machine Learning. Medical operators are still reluctant to rely completely in automated solutions at diagnosis stage. However, Machine Learning researchers have focused their attention in this field, proposing valuable methods having often an outcome comparable to human evaluation. In this paper we give a brief overview on the role of Computer Vision and Machine Learning in solving medical problems in an automatic (supervised or unsupervised) fashion, we consider then a case study of Flow Cytometry data analysis for MRD assessment in Acute Lymphoblastic Leukemia. The clinical evaluation procedure of this type of data consists in a time taking manual labeling that can be performed only after an intensive training, however sometimes different experience may lead to different opinions. We are therefore proposing two different approaches: the first is generative semi-supervised Gaussian Mixture Model based approach, the latter is a discriminative semi-supervised Deep Learning based approach.

References

  1. Aghaeepour, N., Finak, G., Hoos, H., Mosmann, T. R., Brinkman, R., Gottardo, R., Scheuermann, R. H., Consortium, F., Consortium, D., et al. (2013). Critical assessment of automated flow cytometry data analysis techniques. Nature methods, 10(3):228-238.
  2. Bashashati, A. and Brinkman, R. R. (2009). A survey of flow cytometry data analysis methods. Advances in bioinformatics, 2009:584603-584603.
  3. Bengio, Y. (2009). Learning deep architectures for ai. Machine Learning, 2(1):1-127.
  4. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
  5. Cimpoi, M., Maji, S., and Vedaldi, A. (2015). Deep filter banks for texture recognition and segmentation. In Conference on Computer Vision and Pattern Recognition. IEEE.
  6. Conigliaro, D., Rota, P., Setti, F., Bassetti, C., Conci, N., Sebe, N., and Cristani, M. (2015). The s-hock dataset: Analyzing crowds at the stadium. In Conference on Computer Vision and Pattern Recognition. IEEE.
  7. Costa, E., Pedreira, C. E., Barrena, S., Lecrevisse, Q., Flores, J., Quijano, S., Almeida, J., del Carmen GarciaMacias, M., Bottcher, S., Van Dongen, J., et al. (2010). Automated pattern-guided principal component analysis vs expert-based immunophenotypic classification of b-cell chronic lymphoproliferative disorders: a step forward in the standardization of clinical immunophenotyping. Leukemia, 24(11):1927-1933.
  8. Dworzak, M. N., Gaipa, G., Ratei, R., Veltroni, M., Schumich, A., Maglia, O., Karawajew, L., Benetello, A., P ötschger, U., Husak, Z., et al. (2008). Standardization of flow cytometric minimal residual disease evaluation in acute lymphoblastic leukemia: Multicentric assessment is feasible. Cytometry Part B: Clinical Cytometry, 74(6):331-340.
  9. Finak, G., Bashashati, A., Brinkman, R., and Gottardo, R. (2009). Merging mixture components for cell population identification in flow cytometry. Advances in Bioinformatics, 2009.
  10. Gonzalez-Garcia, A., Vezhnevets, A., and Ferrari, V. (2015). An active search strategy for efficient object class detection. In Conference on Computer Vision and Pattern Recognition. IEEE.
  11. Hariharan, B., Arbeláez, P., Girshick, R., and Malik, J. (2014). Hypercolumns for object segmentation and fine-grained localization. arXiv preprint arXiv:1411.5752.
  12. He, K., Zhang, X., Ren, S., and Sun, J. (2014). Spatial pyramid pooling in deep convolutional networks for visual recognition. In European Conference of Computer Vision. Springer.
  13. Hofmanninger, J. and Langs, G. (2015). Mapping visual features to semantic profiles for retrieval in medical imaging. In Conference on Computer Vision and Pattern Recognition. IEEE.
  14. Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097-1105.
  15. Lo, K., Brinkman, R. R., and Gottardo, R. (2008). Automated gating of flow cytometry data via robust modelbased clustering. Cytometry Part A, 73(4):321-332.
  16. Naim, I., Datta, S., Rebhahn, J., Cavenaugh, J. S., Mosmann, T. R., and Sharma, G. (2014). Swift - scalable clustering for automated identification of rare cell populations in large, high-dimensional flow cytometry datasets, part 1: Algorithm design. Cytometry Part A, 85(5):408-421.
  17. Pyne, S., Hu, X., Wang, K., Rossin, E., Lin, T.-I., Maier, L. M., Baecher-Allan, C., McLachlan, G. J., Tamayo, P., Hafler, D. A., et al. (2009). Automated high-dimensional flow cytometric data analysis. Proceedings of the National Academy of Sciences, 106(21):8519-8524.
  18. Qureshi, T. A., Hunter, A., and Al-Diri, B. (2014). A bayesian framework for the local configuration of retinal junctions. In Conference on Computer Vision and Pattern Recognition. IEEE.
  19. Ramanathan, V., Li, C., Deng, J., Han, W., Li, Z., Gu, K., Song, Y., Bengio, S., Rossenberg, C., and Fei-Fei, L. (2015). Learning semantic relationships for better action retrieval in images. In Conference on Computer Vision and Pattern Recognition. IEEE.
  20. Staal, J., Abràmoff, M. D., Niemeijer, M., Viergever, M., Van Ginneken, B., et al. (2004). Ridge-based vessel segmentation in color images of the retina. Medical Imaging, IEEE Transactions on, 23(4):501-509.
  21. Toedling, J., Rhein, P., Ratei, R., Karawajew, L., and Spang, R. (2006). Automated in-silico detection of cell populations in flow cytometry readouts and its application to leukemia disease monitoring. BMC bioinformatics, 7:282-282.
  22. Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., and Manzagol, P.-A. (2010). Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. The Journal of Machine Learning Research, 11:3371-3408.
  23. Yoo, I., Alafaireet, P., Marinov, M., Pena-Hernandez, K., Gopidi, R., Chang, J.-F., and Hua, L. (2012). Data mining in healthcare and biomedicine: a survey of the literature. Journal of medical systems, 36(4):2431- 2448.
  24. Zhang, Y., Sohn, K., Villegas, R., Pan, G., and Lee, H. (2015). Improving object detection with deep convolutional networks via bayesian optimization and structured prediction. In Conference on Computer Vision and Pattern Recognition. IEEE.
  25. Zheng, L., Wang, S., Tian, L., He, F., Liu, Z., and Tian, Q. (2015). Query-adaptive late fusion for image search and person re-identification. In Conference on Computer Vision and Pattern Recognition. IEEE.
  26. Zhou, Y., Chang, H., Barner, K., Spellman, P., and Parvin, B. (2014). Classification of histology sections via multispectral convolutional sparse coding. In Conference on Computer Vision and Pattern Recognition. IEEE.
  27. Zhu, X., Suk, H.-I., and Shen, D. (2014). Matrixsimilarity based loss function and feature selection for alzheimer's disease diagnosis. In Conference on Computer Vision and Pattern Recognition. IEEE.
Download


Paper Citation


in Harvard Style

Rota P., Kleber F., Reiter M., Groeneveld-Krentz S. and Kampel M. (2016). The Role of Machine Learning in Medical Data Analysis. A Case Study: Flow Cytometry . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 303-310. DOI: 10.5220/0005675903030310


in Bibtex Style

@conference{visapp16,
author={Paolo Rota and Florian Kleber and Michael Reiter and Stefanie Groeneveld-Krentz and Martin Kampel},
title={The Role of Machine Learning in Medical Data Analysis. A Case Study: Flow Cytometry},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={303-310},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005675903030310},
isbn={978-989-758-175-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)
TI - The Role of Machine Learning in Medical Data Analysis. A Case Study: Flow Cytometry
SN - 978-989-758-175-5
AU - Rota P.
AU - Kleber F.
AU - Reiter M.
AU - Groeneveld-Krentz S.
AU - Kampel M.
PY - 2016
SP - 303
EP - 310
DO - 10.5220/0005675903030310