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.
Cite
@article{arxiv.2012.01189,
title = {Classifying bacteria clones using attention-based deep multiple instance learning interpreted by persistence homology},
author = {Adriana Borowa and Dawid Rymarczyk and Dorota Ochońska and Monika Brzychczy-Włoch and Bartosz Zieliński},
journal= {arXiv preprint arXiv:2012.01189},
year = {2021}
}
Comments
Published at the International Joint Conferences on Neural Networks