Abstract
The large size of histological images combined with their very challenging appearance are two main difficulties which considerably complicate their analysis. In this paper, we introduce an interactive strategy leveraging the output of a supervised random forest classifier to guide a user through such large visual data. Starting from a forest-based pixelwise estimate, subregions of the images at hand are automatically ranked and sequentially displayed according to their expected interest. After each region suggestion, the user selects among several options a rough estimate of the true amount of foreground pixels in this region. From these one-click inputs, the region scoring function is updated in real time using an online gradient descent procedure, which corrects on-the-fly the shortcomings of the initial model and adapts future suggestions accordingly. Experimental validation is conducted for extramedullary hematopoesis localization and demonstrates the practical feasibility of the procedure as well as the benefit of the online adaptation strategy.
Chapter PDF
Similar content being viewed by others
References
Veta, M., Pluim, J., van Diest, P., Viergever, M.: Breast cancer histopathology image analysis: A review. IEEE Transactions on Biomedical Engineering (2014)
Crowley, R.S., Naus, G.J., Stewart, J., Friedman, C.P.: Development of visual diagnostic expertise in pathology - an information-processing study. Journal of the American Medical Informatics Association 10(1), 39–51 (2003)
Zhao, P., Hoi, S.C.: OTL: A framework of online transfer learning. In: Proceedings of the 27th International Conference on Machine Learning, pp. 1231–1238 (2010)
Tommasi, T., Orabona, F., Kaboli, M., Caputo, B.: Leveraging over prior knowledge for online learning of visual categories. In: BMVC (2012)
Jain, V., Learned-Miller, E.: Online domain adaptation of a pre-trained cascade of classifiers. In: CVPR, pp. 577–584 (2011)
Breiman, L.: Random forests. Machine Learning (2001)
Criminisi, A., Shotton, J.: Decision Forests for Computer Vision and Medical Image Analysis. Springer (2013)
Saffari, A., Leistner, C., Santner, J., Godec, M., Bischof, H.: On-line random forests. In: ICCV Workshops, pp. 1393–1400 (September 2009)
Hastie, T., Tibshirani, R., Friedman, J.: The elements of statistical learning: data mining, inference and prediction, 2nd edn. Springer (2009)
Shalev-Shwartz, S.: Online learning and online convex optimization. Found. Trends Mach. Learn. 4(2), 107–194 (2012)
Tao, K., Fang, M., Alroy, J., Sahagian, G.: Imagable 4t1 model for the study of late stage breast cancer. BMC Cancer 8(1) (2008)
Winn, J., Criminisi, A., Minka, T.: Object categorization by learned universal visual dictionary. In: ICCV (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Peter, L. et al. (2014). Leveraging Random Forests for Interactive Exploration of Large Histological Images. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. MICCAI 2014. Lecture Notes in Computer Science, vol 8673. Springer, Cham. https://doi.org/10.1007/978-3-319-10404-1_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-10404-1_1
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10403-4
Online ISBN: 978-3-319-10404-1
eBook Packages: Computer ScienceComputer Science (R0)