论文标题
确定性汇总多个实例学习
Certainty Pooling for Multiple Instance Learning
论文作者
论文摘要
多个实例学习是一种弱监督学习的形式，其中将数据安排在一个名为袋子的实例中，每个袋子分配一个标签。 Bag Level类预测是通过在实例预测或嵌入中应用置换不变池操作员的多个实例得出的。我们提出了一个名为\ textbf {确定性池}的新颖的合并操作员，该操作员将模型确定性纳入袋子预测中，从而产生了更健壮和可解释的模型。我们将我们提出的方法与其他合并操作员在受控实验中，基于MNIST以及现实生活组织病理学数据集-CamelyOn16的受控实验。我们的方法在行李级和实例级别的预测中都优于其他方法，尤其是在只有小训练集时。我们讨论了我们的方法背后的理由及其对这些类型数据集优势的原因。
Multiple Instance Learning is a form of weakly supervised learning in which the data is arranged in sets of instances called bags with one label assigned per bag. The bag level class prediction is derived from the multiple instances through application of a permutation invariant pooling operator on instance predictions or embeddings. We present a novel pooling operator called \textbf{Certainty Pooling} which incorporates the model certainty into bag predictions resulting in a more robust and explainable model. We compare our proposed method with other pooling operators in controlled experiments with low evidence ratio bags based on MNIST, as well as on a real life histopathology dataset - Camelyon16. Our method outperforms other methods in both bag level and instance level prediction, especially when only small training sets are available. We discuss the rationale behind our approach and the reasons for its superiority for these types of datasets.
