In many real-world applications, one object (e.g., image) can be represented
or described by multiple instances (e.g., image patches) and simultaneously
associated with multiple labels. Such applications can be formulated as
multi-instance multi-label learning (MIML) problems and have been extensively
studied during the past few years. Existing MIML methods are useful in many
applications but most of which suffer from relatively low accuracy and training
efficiency due to several issues: i) the inter-label correlations (i.e., the
probabilistic correlations between the multiple labels corresponding to an
object) are neglected; ii) the inter-instance correlations cannot be learned
directly (or jointly) with other types of correlations due to the missing
instance labels; iii) diverse inter-correlations (e.g., inter-label
correlations, inter-instance correlations) can only be learned in multiple
stages. To resolve these issues, a new single-stage framework called broad
multi-instance multi-label learning (BMIML) is proposed. In BMIML, there are
three innovative modules: i) an auto-weighted label enhancement learning
(AWLEL) based on broad learning system (BLS); ii) A specific MIML neural
network called scalable multi-instance probabilistic regression (SMIPR); iii)
Finally, an interactive decision optimization (IDO). As a result, BMIML can
achieve simultaneous learning of diverse inter-correlations between whole
images, instances, and labels in single stage for higher classification
accuracy and much faster training time. Experiments show that BMIML is highly
competitive to (or even better than) existing methods in accuracy and much
faster than most MIML methods even for large medical image data sets (> 90K
images).

该研究提出了一种名为 BMIML 的新单阶段框架，它包含三个创新模块：基于 BLS 的自动加权标签增强学习、可缩放多实例概率回归（SMIPR）和交互式决策优化（IDO），可以在单个阶段中实现整个图像、实例和标签之间的多样互相关系学习，以提高分类准确性并快速训练，同时在准确性方面高度竞争，并且速度比大多数 MIML 方法快得多，甚至对于大型医学图像数据集（>90K 图像）也是如此。