Discriminative Feature-oriented Dictionary Learning for histopathological image classification



In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structure. In this paper, we propose an automatic feature discovery framework for extracting discriminative class-specific features and present a low-complexity method for classification and disease grading in histopathology. Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific features which are suitable for representing samples from the same class while are poorly capable of representing samples from other classes. Experiments on three challenging real-world image databases: 1) histopathological images of intraductal breast lesions, 2) mammalian kidney, lung and spleen images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, reveal the merits of our proposal over state-of-the-art alternatives in a variety problems. Moreover, we demonstrate that DFDL exhibits a more graceful decay in classification accuracy against the number of training images which is highly desirable in practice where generous training per class is often not available.

Data Sets

Three different histopathological image data sets are discussed in this project:

  1. ADL Data Sets

  2. IBL Data Sets

  3. TCGA Data Sets

Description of these data sets could be found here.

Samples from ADL - Kidney dataset could be found here.

Source Code

Source code for DFDL could be found at Mathworks File Exchange or Github.

Classification Procedure

Figure. IBL/ADL classification procedure [high resolution version]

Example Learned Bases

Figure. Example bases learned from different dictionary learning methods [high resolution version]

Selected Results

Figure. Overall classification accuracies of: left - IBL and ADL data sets; right - TCGA data set

Related Publications

  1. Tiep H. Vu, H. S. Mousavi, V. Monga, A. U. Rao and G. Rao, "Histopathological Image Classification using Discriminative Feature-Oriented Dictionary Learning", IEEE Transactions on Medical Imaging , volume 35, issue 3, pages 738-751, March 2016. [arXiv]

  2. Tiep H. Vu, H. S. Mousavi, V. Monga, A. U. Rao and G. Rao, "DFDL: Discriminative Feature-Oriented Dictionary Learning For Histopathological Image Classification", IEEE International Symposium on Biomedical Imaging (ISBI), Brooklyn, NY, USA,  April 16th-19th, 2015. [IEEE Xplore] [arXiv] , [ISBI_poster]

Selected References

  1. U. Srinivas, H. S. Mousavi, V. Monga, A. Hattel, and B. Jayarao, "Simultaneous sparsity model for histopathological image representation and classification," IEEE Trans. on Medical Imaging, vol. 33, no. 5, pp. 1163-1179, May 2014.

  2. Z. Jiang, Z. Lin, and L. Davis, "Label consistent K-SVD: Learning a discriminative dictionary for recognition," IEEE Trans. on Pattern Analysis and Machine Int. , vol. 35, no. 11, pp. 2651-2664, 2013

  3. M. Yang, L. Zhang, X. Feng, and D. Zhang, "Fisher discrimination dictionary learning for sparse representation,"in Proc. IEEE Conf. on Computer Vision , Nov. 2011, pp. 543-550.

  4. N. Nayak, H. Chang, A. Borowsky, P. Spellman, and B. Parvin, "Classification of tumor histopathology via sparse feature learning," in Proc. IEEE Int. Symp. Biomed. Imag. , 2013, pp. 1348-1351.

  5. N. Orlov, L. Shamir, T. Macuraand, J. Johnston, D. Eckley, and I. Goldberg, "WND-CHARM: Multi-purpose image classification using compound image transforms," Pattern Recogn. Lett., vol. 29, no. 11, pp. 1684-1693, 2008.


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