Recently, a lot of automated white blood cells (WBC) or leukocyte classification techniques have been developed. However, all of these methods only utilize a single modality microscopic image i.e. either blood smear or fluorescence based, thus missing the potential of a better learning from multimodal images. In this work, we develop an efficient multimodal architecture based on a first of its kind multimodal WBC dataset for the task of WBC classification. Specifically, our proposed idea is developed in two steps - 1) First, we learn modality specific independent subnetworks inside a single network only; 2) We further enhance the learning capability of the independent subnetworks by distilling knowledge from high complexity independent teacher networks. With this, our proposed framework can achieve a high performance while maintaining low complexity for a multimodal dataset. Our unique contribution is two-fold - 1) We present a first of its kind multimodal WBC dataset for WBC classification; 2) We develop a high performing multimodal architecture which is also efficient and low in complexity at the same time.

翻译：最近,开发了许多自动化白血球(WBC)或白血球分类技术。然而,所有这些方法都只使用单一模式的微观图象,即基于血液涂片或荧光,从而失去了从多式图象中更好地学习的潜力。在这项工作中,我们开发了一个高效的多式联运结构,其基础是用于WBC分类工作的首个多式WBC多式数据集。具体地说,我们提出的构想分两个步骤:(1) 首先,我们只在一个网络中学习特定模式的独立的子网络;(2) 我们通过从高度复杂的独立教师网络中提取知识,进一步提高独立子网络的学习能力。 有了这个方法,我们拟议的框架可以取得高性能,同时保持一个多式数据集的低复杂性。 我们的独特贡献有两重:(1) 我们提出了第一个用于WBC分类的多式WBC多式多式数据库;(2) 我们开发了一个高效和低复杂性的高级多式联运结构。