Analysis-Synthesis Learning With Shared Features: Algorithms for Histology Image Classification

Objective: The diversity of tissue structure in histopathological images makes feature extraction for classification a challenging task. Dictionary learning within a sparse representation-based classification (SRC) framework has been shown to be successful for feature discovery. However, there exist stiff practical challenges: 1) computational complexity of SRC can be onerous in the decision stage since it involves solving a sparsity constrained optimization problem and often over a large number of image patches; and 2) images from distinct classes continue to share several geometric features. We propose a novel analysis-synthesis model learning with shared features algorithm (ALSF) for classifying such images more effectively. Methods: In the ALSF, a joint analysis and synthesis learning model is introduced to learn the classifier and the feature extractor at the same time. Unlike SRC, no explicit optimization is needed in the inference phase leading to much reduced computation. Crucially, we introduce the learning of a low-rank shared dictionary and a shared analysis operator, which more accurately represents both similarities and differences in histopathological images from distinct classes. We also develop an extension of ALSF with a sparsity constraint, whose presence or absence facilitates a cost-performance tradeoff. Results: The ALSF is evaluated on three challenging and well-known datasets: 1) spleen tissue images; 2) brain tumor images; and 3) breast cancer tissue dataset, provided by different organizations. Conclusion: Experimental results demonstrate both complexity and performance benefits of the ALSF over state-of-the-art alternatives. Significance: Modeling shared features with appropriate quantitative constraints lead to significantly improved classification in histopathology.