Microtitre Plate Image Augmentation with Generative Adversarial Networks

Abstract

Antibiotic Susceptibility Testing (AST) based on microorganism culturing is the gold-standard technique to determine whether a pathogen is susceptible or resistant to available antibiotics. While broth microdilution offers a potential high-throughput method for AST, reading and interpreting microtitre plates can be challenging, even for experienced clinical microbiologists. Machine learning models trained on images of microtitre plates obtained during AST could potentially accelerate and even automate this process. However, these image sets are highly imbalanced since each drug on the plate may exhibit different growth distributions due to varying resistance prevalence and mechanisms, which negatively impacts the performance of trained models. To address this problem, we propose a Generative Adversarial Network (GAN)-based framework, named CulplateGAN, to augment the dataset with images of plates displaying specific growth levels for particular drugs. The adversarial loss and weight-controlled content loss are introduced to achieve image transformation and content preservation. Moreover, a Multi-Culplate-GAN architecture is designed to generate multilevel outputs with one single input, which are optimized by the proposed domain-based adversarial loss and domain classification loss. We evaluate Culplate-GAN and Multi-Culplate-GAN by training a classifier on an AST Mycobacterium Tuberculosis dataset. Comprehensive results indicate that our method outperforms existing representative augmentation methods and can be generalized to plates containing other bacterial cultures.