Multiple Instance Learning (MIL) is a sub-domain of classification problems with positive and negative labels and a "bag" of inputs, where the label is positive if and only if a positive element is contained within the bag, and otherwise is negative. Training in this context requires associating the bag-wide label to instance-level information, and implicitly contains a causal assumption and asymmetry to the task (i.e., you can't swap the labels without changing the semantics). MIL problems occur in healthcare (one malignant cell indicates cancer), cyber security (one malicious executable makes an infected computer), and many other tasks. In this work, we examine five of the most prominent deep-MIL models and find that none of them respects the standard MIL assumption. They are able to learn anti-correlated instances, i.e., defaulting to "positive" labels until seeing a negative counter-example, which should not be possible for a correct MIL model. We suspect that enhancements and other works derived from these models will share the same issue. In any context in which these models are being used, this creates the potential for learning incorrect models, which creates risk of operational failure. We identify and demonstrate this problem via a proposed "algorithmic unit test", where we create synthetic datasets that can be solved by a MIL respecting model, and which clearly reveal learning that violates MIL assumptions. The five evaluated methods each fail one or more of these tests. This provides a model-agnostic way to identify violations of modeling assumptions, which we hope will be useful for future development and evaluation of MIL models.

翻译：多示例学习（MIL）是分类问题的一个子领域，其输入包含正负标签以及由多个示例组成的“包”。当且仅当包中包含正样本时，该包被标记为正，否则为负。在此背景下的训练需要将包的标签与实例级信息相关联，并隐含了因果假设和任务的不对称性（即，交换标签会改变语义）。MIL问题出现在医疗健康（一个恶性细胞即表明癌症）、网络安全（一个恶意可执行文件即可导致计算机感染）以及许多其他任务中。本研究对五种最突出的深度MIL模型进行了检验，发现它们均未遵循标准MIL假设。这些模型能够学习反相关实例，即默认输出“正”标签，直到遇到负反例才改变——这对于正确的MIL模型而言本不应发生。我们推测，基于这些模型的改进方法及其他衍生工作也将存在相同问题。若在任何使用这些模型的场景中，这种缺陷可能导致学习到错误模型，从而带来运行失败的风险。我们通过提出的“算法单元测试”识别并验证了该问题：该方法创建了可由遵循MIL假设的模型求解的合成数据集，并清晰揭示了违反MIL假设的学习行为。五种被评估的方法均未能通过其中一项或多项测试。这提供了一种与模型无关的识别建模假设违反情况的方法，有望为未来MIL模型的发展与评估提供帮助。