PAS-Net: Rapid Prediction of Antibiotic Susceptibility from Fluorescence Images of Bacterial Cells Using Parallel Dual-Branch Network

Xiong, Wei1; Yu, Kaiwei2; Yang, Liang2; Lei, Baiying1

10.1007/978-3-031-43993-3_56

2023

26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

0302-9743

1611-3349

978-3-031-43992-6

MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2023, PT VIII

14227

OCT 08-12, 2023

null,Vancouver,CANADA

GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND

SPRINGER INTERNATIONAL PUBLISHING AG

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.

Parallel dual-branch network Feature interaction unit Hierarchical multi-head self-attention Antibiotic susceptibility prediction

其他

英语

EI ; CPCI-S

National Natural Science Foundation of China["62101338","61871274","32270196","U1902209"]

Computer Science ; Neurosciences & Neurology ; Radiology, Nuclear Medicine & Medical Imaging

Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods ; Neuroimaging ; Radiology, Nuclear Medicine & Medical Imaging

WOS:001109637500056

Web of Science

1.National-Regional Key Technology Engineering Laboratory for MedicalUltrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen; 518060, China
2.School of Medicine, Southern University of Science and Technology, Shenzhen; 518055, China

Xiong, Wei,Yu, Kaiwei,Yang, Liang,et al. PAS-Net: Rapid Prediction of Antibiotic Susceptibility from Fluorescence Images of Bacterial Cells Using Parallel Dual-Branch Network[C]. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND:SPRINGER INTERNATIONAL PUBLISHING AG,2023.