Advertisement

Quality Classification of Microscopic Imagery with Weakly Supervised Learning

  • Xinghua Lou
  • Luca Fiaschi
  • Ullrich Koethe
  • Fred A. Hamprecht
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7588)

Abstract

In this post-genomic era, microscopic imaging is playing a crucial role in biomedical research and important information is to be discovered by quantitatively mining the resulting massive imagery databases. To this end, an important prerequisite is robust, high quality imagery databases. This is because defect images will jeopardize downstream tasks such as feature extraction and statistical analysis, yielding misleading results or even false conclusions. This paper presents a weakly supervised learning framework to tackle this problem. Our framework resembles a cascade of classifiers with feature and similarity measure designed for both global and local defects. We evaluated the framework on a database of images and obtained a 96.9% F-score for the important normal class. Click-and-play open source software is provided.

Keywords

Training Image Outlier Detection Regional Defect Defect Image Original Feature Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bray, M.A., Fraser, A.N., Hasaka, T.P., et al.: Workflow and Metrics for Image Quality Control in Large-Scale High-Content Screens. J. Biomol. Screening (2011)Google Scholar
  2. 2.
    Breunig, M.M., Kriegel, H.P., Ng, R.T.J., et al.: LOF: identifying density-based local outliers. ACM Sigmod Record 29(2), 93–104 (2000)CrossRefGoogle Scholar
  3. 3.
    Chandola, V., Banerjee, A., Kumar, V.: Outlier detection: A survey. ACM Comput. Surv. (2007)Google Scholar
  4. 4.
    Echeverri, C.J., Perrimon, N.: High-throughput RNAi screening in cultured cells: a user’s guide. Nat. Rev. Genet. 7(5), 373–384 (2006)CrossRefGoogle Scholar
  5. 5.
    Goode, A., Sukthankar, R., Mummert, L., et al.: Distributed online anomaly detection in high-content screening. In: ISBI (2008)Google Scholar
  6. 6.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer Series in Statistics. Springer New York Inc., New York (2001)zbMATHGoogle Scholar
  7. 7.
    Hero, A.O.: Geometric entropy minimization (GEM) for anomaly detection and localization. In: NIPS (2006)Google Scholar
  8. 8.
    Kaynig, V., Fischer, B., Buhmann, J.M.: Probabilistic image registration and anomaly detection by nonlinear warping. In: CVPR (2008)Google Scholar
  9. 9.
    Knox, E.M., Ng, R.T.: Algorithms for mining distance-based outliers in large datasets. In: VLDB (1998)Google Scholar
  10. 10.
    Lazarevic, A., Kumar, V.: Feature bagging for outlier detection. In: KDD (2005)Google Scholar
  11. 11.
    Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation Forest. In: ICDM (2008)Google Scholar
  12. 12.
    Liu, R., Li, Z., Jia, J.: Image partial blur detection and classification. In: CVPR (2008)Google Scholar
  13. 13.
    MAQC Consortium The MicroArray Quality Control (MAQC) project shows inter-and intraplatform reproducibility of gene expression measurements. Nat. Biotechnol. 24(9), 1151–1161 (2006)Google Scholar
  14. 14.
    Pele, O., Werman, M.: Fast and robust earth mover’s distances. In: ICCV (2009)Google Scholar
  15. 15.
    Pepperkok, R., Ellenberg, J.: High-throughput fluorescence microscopy for systems biology. Nat. Rev. Mol. Cell Bio. 7(9), 690–696 (2006)CrossRefGoogle Scholar
  16. 16.
    Reymann, J., Beil, N., Beneke, J., et al.: Next-generation 9216-microwell cell arrays for high-content screening microscopy. Bio.Techniques 47(4), 877 (2009)Google Scholar
  17. 17.
    Rubner, Y., Tomasi, C., Guibas, L.J.: A Metric for Distributions with Applications to Image Databases. In: ICCV (1998)Google Scholar
  18. 18.
    Schoelkopf, B., Platt, J.C., Shawe-Taylor, J., et al.: Estimating the support of a high-dimensional distribution. Neural. Comput. 13(7), 1443–1471 (2001)zbMATHCrossRefGoogle Scholar
  19. 19.
    Schoelkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. The MIT Press, Cambridge (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xinghua Lou
    • 1
  • Luca Fiaschi
    • 1
  • Ullrich Koethe
    • 1
  • Fred A. Hamprecht
    • 1
  1. 1.HCI, IWRUniversity of HeidelbergGermany

Personalised recommendations