Leveraging Random Forests for Interactive Exploration of Large Histological Images

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.

[1]  Barbara Caputo,et al.  Leveraging over prior knowledge for online learning of visual categories , 2012, BMVC.

[2]  Max A. Viergever,et al.  Breast Cancer Histopathology Image Analysis: A Review , 2014, IEEE Transactions on Biomedical Engineering.

[3]  Antonio Criminisi,et al.  Decision Forests for Computer Vision and Medical Image Analysis , 2013, Advances in Computer Vision and Pattern Recognition.

[4]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[5]  Steven C. H. Hoi,et al.  OTL: A Framework of Online Transfer Learning , 2010, ICML.

[6]  J. Franklin,et al.  The elements of statistical learning: data mining, inference and prediction , 2005 .

[7]  Horst Bischof,et al.  On-line Random Forests , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[8]  Shai Shalev-Shwartz,et al.  Online Learning and Online Convex Optimization , 2012, Found. Trends Mach. Learn..

[9]  Joseph,et al.  Imagable 4T1 model for the study of late stage breast cancer , 2008, BMC Cancer.

[10]  Charles P. Friedman,et al.  Development of visual diagnostic expertise in pathology -- an information-processing study. , 2003, Journal of the American Medical Informatics Association : JAMIA.

[11]  Antonio Criminisi,et al.  Object categorization by learned universal visual dictionary , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[12]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[13]  Erik G. Learned-Miller,et al.  Online domain adaptation of a pre-trained cascade of classifiers , 2011, CVPR 2011.