Deep Clustering for Image Data: Methods and Applications

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Clustering is a common task in image processing and machine learning fields. This unsupervised learning technique investigates similarities in data points and studies how similar data points are naturally grouped together. Clustering problems have been extensively studied in the literature for traditional settings. However, clustering noisy, high-dimensional data points remains an open problem because many assumptions of standard algorithms may not hold true in the high-dimensional setting. Although deep learning has pervaded almost all aspects of machine learning, there have been relatively limited publications on deep clustering. The limited research on deep learning based clustering motivates us to carry out further research. The current research program will focus on deep clustering for image data analysis by both establishing theoretical foundations and developing model-specific and application-specific novel algorithms. Specifically, the proposed program will pursue the following main technical objectives: 1) Investigating joint deep learning and clustering: Apart from the stacked autoencoder, since other deep learning models are largely unexplored in deep clustering, we will investigate clustering performance with other popular deep learning models (e.g., convolutional neural networks (CNN), deep Boltzmann machines (DBM) and the developing deep dictionary learning (DDL)). We will develop end-to-end learning frameworks for joint deep learning-based embedding and clustering. We will investigate different deep learning models, clustering approaches, objective functions and training/optimization strategies. We will also address the model interpretability issue in deep clustering. 2) Investigating medical image analysis applications: Clustering of medical images can have many applications in medical imaging. Based on the applicant's current research focus, to test the proposed deep clustering methods and investigate their applicability to real-world medical problems, we will particularly investigate medical image segmentation and medical medical image retrieval applications. By applying the algorithms developed in this work on real-world problems in medical imaging, we will highlight their practical utility and understand their limitations. We will also need to develop application-specific algorithms given additional constraints/challenges in practice. The significance of this research lies in its focus on both the model and practice of deep clustering of high dimensional data. The outcome of this research program will make significant contributions to both the clustering methods and applications of big image data analytics. As one key innovation accelerator, big data analytics is revolutionizing many research and industry areas. The proposed research will help take this vision one tiny step further. The research program will provide an opportunity for the graduate students to be trained in related cutting-edge technologies.