论文标题
使用基于注意力的深层实例学习通过持久性同源性解释的细菌克隆分类
Classifying bacteria clones using attention-based deep multiple instance learning interpreted by persistence homology
论文作者
论文摘要
在这项工作中，我们分析仅基于显微镜图像的同一细菌种类（克雷伯氏菌肺炎）的不同克隆。这是一项具有挑战性的任务，由于克隆的相似性，以前被认为是不可能的。为此，我们将多步算法应用于基于注意力的多个实例学习。除了在0.9的水平上获得准确性外，我们还基于细胞生理学和持久性同源性引入了广泛的可解释性，从而提高了模型的可理解性和信任。
In this work, we analyze if it is possible to distinguish between different clones of the same bacteria species (Klebsiella pneumoniae) based only on microscopic images. It is a challenging task, previously considered impossible due to the high clones similarity. For this purpose, we apply a multi-step algorithm with attention-based multiple instance learning. Except for obtaining accuracy at the level of 0.9, we introduce extensive interpretability based on CellProfiler and persistence homology, increasing the understandability and trust in the model.
