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

arXiv: https://arxiv.org/abs/2012.01189 · HTML: https://mlsanity.com/paper/2012.01189/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2020.

## Built on by (most cited first, 2 of 2)

- [AI-Driven Rapid Identification of Bacterial and Fungal Pathogens in Blood Smears of Septic Patients](https://mlsanity.com/paper/2503.14542/) (2025)
- [Semi-Automatic Labeling and Semantic Segmentation of Gram-Stained Microscopic Images from DIBaS Dataset](https://mlsanity.com/paper/2208.10737/) (2022)

## Abstract (arXiv)

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.