Multi-instance learning (MIL) is a widely-applied technique in practical
applications that involve complex data structures. MIL can be broadly
categorized into two types: traditional methods and those based on deep
learning. These approaches have yielded significant results, especially with
regards to their problem-solving strategies and experimental validation,
providing valuable insights for researchers in the MIL field. However, a
considerable amount of knowledge is often trapped within the algorithm, leading
to subsequent MIL algorithms that solely rely on the model's data fitting to
predict unlabeled samples. This results in a significant loss of knowledge and
impedes the development of more intelligent models. In this paper, we propose a
novel data-driven knowledge fusion for deep multi-instance learning (DKMIL)
algorithm. DKMIL adopts a completely different idea from existing deep MIL
methods by analyzing the decision-making of key samples in the data set
(referred to as the data-driven) and using the knowledge fusion module designed
to extract valuable information from these samples to assist the model's
training. In other words, this module serves as a new interface between data
and the model, providing strong scalability and enabling the use of prior
knowledge from existing algorithms to enhance the learning ability of the
model. Furthermore, to adapt the downstream modules of the model to more
knowledge-enriched features extracted from the data-driven knowledge fusion
module, we propose a two-level attention module that gradually learns shallow-
and deep-level features of the samples to achieve more effective
classification. We will prove the scalability of the knowledge fusion module
while also verifying the efficacy of the proposed architecture by conducting
experiments on 38 data sets across 6 categories.

本文提出了基于数据驱动知识融合的深度多实例学习算法（DKMIL），该算法采用新的模型数据接口，通过知识融合模块从样本中提取有价值的信息以辅助模型训练，并使用两级注意力模块逐步学习样本的浅层和深层特征以实现更有效的分类。在对 6 个类别的 38 个数据集进行实验的同时证明了知识融合模块的可扩展性和所提出体系结构的有效性。