Thanks to the significant developments in healthcare industries, various types of medical data are generated. Analysing such valuable resources aid healthcare experts to understand the illnesses more precisely and provide better clinical services. Machine learning as one of the capable tools could assist healthcare experts in achieving expressive interpretation and making proper decisions. As annotation of medical data is a costly and sensitive task that can be performed just by healthcare professionals, label-free methods could be significantly promising. Interpretability and evidence-based decision are other concerns in medicine. These needs were our motivators to propose a novel clustering method based on hierarchical Dirichlet process mixtures of multivariate Beta distributions. To learn it, we applied batch and online variational methods for finding the proper number of clusters as well as estimating model parameters at the same time. The effectiveness of the proposed models is evaluated on three medical real applications, namely oropharyngeal carcinoma diagnosis, osteosarcoma analysis, and white blood cell counting.

由于医疗保健行业的重大发展，产生了各种类型的医疗数据。分析这些宝贵的资源有助于医疗保健专家更准确地了解疾病并提供更好的临床服务。机器学习作为一种强大的工具，可以帮助医疗保健专家实现富有表现力的解释并做出正确的决定。由于医疗数据的注释是一项只能由医疗保健专业人员执行的昂贵且敏感的任务，因此无标签方法可能非常有前途。可解释性和基于证据的决定是医学中的其他问题。这些需求是我们提出一种基于多元 Beta 分布的分层狄利克雷过程混合的新型聚类方法的动力。要学习它，我们应用批量和在线变分方法来寻找合适的集群数量并同时估计模型参数。在三个医学实际应用中评估了所提出模型的有效性，即口咽癌诊断、骨肉瘤分析和白细胞计数。