Multiple Instance Learning is a weakly supervised learning technique which is particularly well suited for medical data analysis as the class labels are often not available at desired granularity. Multiple Instance Learning through Deep Neural Networks is relatively a new paradigm in machine learning. The most important part of Multiple Instance Learning through Deep Neural Networks is designing a trainable pooling function which determines the instance-to bag relationship. In this paper, we propose a Multiple Instance pooling technique based on Genetic Algorithm called Genetic Pooling. In this technique, instance labels inside a bag are optimized by minimizing bag-level losses. The main contribution of the paper is that the bag level pooling layer for generating attention weights for bag instances are replaced by random initialization of attention weights and finding the optimized attention weights through Genetic Algorithm.

多实例学习是一种弱监督学习技术，特别适合于医学数据分析，因为通常无法以所需的粒度获得类标签。通过深度神经网络进行多实例学习是机器学习中的一个相对较新的范例。通过深度神经网络进行多实例学习的最重要部分是设计一种可训练的合并功能，该功能确定实例与包的关系。在本文中，我们提出了一种基于遗传算法的称为遗传池的多实例池技术。在这种技术中，通过最小化袋子级别的损失来优化袋子内的实例标签。