Abstract
In recent years, the emergence and rapid spread of multi-drug resistant bacteria has become a serious threat to global public health. Antibiotic susceptibility testing (AST) is used clinically to determine the susceptibility of bacteria to antibiotics, thereby guiding physicians in the rational use of drugs as well as slowing down the process of bacterial resistance. However, traditional phenotypic AST methods based on bacterial culture are time-consuming and laborious (usually 24–72 h). Because delayed identification of drug-resistant bacteria increases patient morbidity and mortality, there is an urgent clinical need for a rapid AST method that allows physicians to prescribe appropriate antibiotics promptly. In this paper, we present a parallel dual-branch network (i.e., PAS-Net) to predict bacterial antibiotic susceptibility from fluorescent images. Specifically, we use the feature interaction unit (FIU) as a connecting bridge to align and fuse the local features from the convolutional neural network (CNN) branch (C-branch) and the global representations from the Transformer branch (T-branch) interactively and effectively. Moreover, we propose a new hierarchical multi-head self-attention (HMSA) module that reduces the computational overhead while maintaining the global relationship modeling capability of the T-branch. PAS-Net is experimented on a fluorescent image dataset of clinically isolated Pseudomonas aeruginosa (PA) with promising prediction performance. Also, we verify the generalization performance of our algorithm in fluorescence image classification on two HEp-2 cell public datasets.
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Acknowledgement
This work was supported National Natural Science Foundation of China (Nos. 62101338, 61871274, 32270196 and U1902209), National Natural Science Foundation of Guangdong Province (2019A1515111205), Shenzhen Key Basic Research Project (KCXFZ20201221173213036, JCYJ20220818095809021, SGDX202011030958020–07, JCYJ201908081556188–06, and JCYJ20190808145011 -259), Shenzhen Peacock Plan Team Project (grants number KQTD20200909113758–004).
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Xiong, W., Yu, K., Yang, L., Lei, B. (2023). PAS-Net: Rapid Prediction of Antibiotic Susceptibility from Fluorescence Images of Bacterial Cells Using Parallel Dual-Branch Network. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14227. Springer, Cham. https://doi.org/10.1007/978-3-031-43993-3_56
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