Classifying bacteria clones using attention-based deep multiple instance learning interpreted by persistence homology

In this work, we analyze if it is possible to distinguish between different clones of the same bacteria species (Klebisiella 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.