Deep Clustering for Image Data: Methods and Applications

图像数据的深度聚类：方法与应用

• 批准号: RGPIN-2019-03981
• 负责人: Ward, Rabab
• 金额: $ 4.66万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752309
• 关键词: Deep; Clustering; Image; Data; Methods

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.

聚类是图像处理和机器学习领域中的一项常见任务。这种无监督学习技术调查数据点的相似性，并研究相似的数据点是如何自然地组合在一起的。在传统设置的文献中，已经广泛地研究了集群问题。然而，对有噪声的高维数据点进行聚类仍然是一个悬而未决的问题，因为标准算法的许多假设在高维环境下可能不成立。虽然深度学习已经渗透到机器学习的几乎所有方面，但关于深度聚类的出版物相对有限。基于深度学习的聚类研究的局限性促使我们开展更深入的研究。目前的研究计划将专注于图像数据分析的深度聚类，既建立理论基础，又开发特定于模型和特定应用的新算法。具体地说，该计划将追求以下主要技术目标：1)研究联合深度学习和聚类：除了堆叠的自动编码器之外，由于其他深度学习模型在深度聚类中基本上是未被探索的，我们将使用其他流行的深度学习模型(例如卷积神经网络(CNN)、深度Boltzmann机器(DBM)和发展中的深度词典学习(DDL))来研究聚类性能。我们将开发端到端的学习框架，用于基于联合深度学习的嵌入和聚类。我们将研究不同的深度学习模型、聚类方法、目标函数和训练/优化策略。我们还将解决深度聚类中的模型可解释性问题。2)研究医学图像分析应用：医学图像聚类在医学成像中有很多应用。基于申请人目前的研究重点，为了测试所提出的深度聚类方法，并考察它们在实际医学问题中的适用性，我们将特别研究医学图像分割和医学图像检索的应用。通过将在这项工作中开发的算法应用于医学成像中的实际问题，我们将突出它们的实用价值，并了解它们的局限性。考虑到实践中的额外限制/挑战，我们还需要开发特定于应用程序的算法。本研究的意义在于对高维数据深度聚类的模型和实践进行了研究。这一研究项目的成果将对大图像数据分析的聚类方法和应用做出重大贡献。作为一个关键的创新加速器，大数据分析正在给许多研究和行业领域带来革命性的变化。这项拟议的研究将有助于将这一愿景向前推进一小步。该研究项目将为研究生提供接受相关尖端技术培训的机会。