Hybrid generative-discriminative nucleus classification of renal cell carcinoma

In this paper, we propose to use advanced classification techniques with shape features for nuclei classification in tissue microarray images of renal cell carcinoma. Our aim is to improve the classification accuracy in distinguishing between healthy and cancerous cells. The approach is inspired by natural language processing: several features are extracted from the automatically segmented nuclei and quantized to visual words, and their co-occurrences are encoded as visual topics. To this end, a generative model, the probabilistic Latent Semantic Analysis (pLSA) is learned from quantized shape descriptors (visual words). Finally, we extract from the learned models a generative score, that is used as input for new classifiers, defining a hybrid generative-discriminative classification algorithm. We compare our results with the same classifiers on the feature set to assess the increase of accuracy when we apply pLSA. We demonstrate that the feature space created using pLSA achieves better accuracies than the original feature space.