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.

翻译：在这项工作中,我们分析能否仅仅根据微观图像区分同一细菌种(Klebsiella肺炎)的不同克隆体(Klebsiella肺炎),这是一项具有挑战性的任务,以前由于高克隆体的相似性而被认为是不可能完成的。为此,我们采用了多步骤算法,以关注为基础多例学习。除了获得0.9级的准确性外,我们采用了基于细胞质素和持久性同质学的广泛解释,提高了对模型的可理解性和信任度。