In many real-world tasks, the concerned objects can be represented as a multi-instance bag associated with a candidate label set, which consists of one ground-truth label and several false positive labels. Multi-instance partial-label learning (MIPL) is a learning paradigm to deal with such tasks and has achieved favorable performances. Existing MIPL approach follows the instance-space paradigm by assigning augmented candidate label sets of bags to each instance and aggregating bag-level labels from instance-level labels. However, this scheme may be suboptimal as global bag-level information is ignored and the predicted labels of bags are sensitive to predictions of negative instances. In this paper, we study an alternative scheme where a multi-instance bag is embedded into a single vector representation. Accordingly, an intuitive algorithm named DEMIPL, i.e., Disambiguated attention Embedding for Multi-Instance Partial-Label learning, is proposed. DEMIPL employs a disambiguation attention mechanism to aggregate a multi-instance bag into a single vector representation, followed by a momentum-based disambiguation strategy to identify the ground-truth label from the candidate label set. Furthermore, we introduce a real-world MIPL dataset for colorectal cancer classification. Experimental results on benchmark and real-world datasets validate the superiority of DEMIPL against other well-established MIPL and partial-label learning methods. Our code and datasets will be made publicly available.

本篇研究提出了一种名为DEMIPL的算法，采用消歧关注机制将多实例袋子(AKA多袋)转换为单一向量表示，并通过动量消歧策略在候选标签集中识别地面真相标签，为肠癌分类研究提供了一个真实世界的MIPL数据集，并在基准和真实世界的数据集中获得了比其他著名MIPL和部分标签学习方法更卓越的实验结果。

多实例局部标签学习的消歧注意嵌入