论文标题
深度学习以检测细菌菌落以生产疫苗
Deep Learning to Detect Bacterial Colonies for the Production of Vaccines
论文作者
论文摘要
在疫苗的开发过程中，对细菌菌落形成单位（CFU）进行计数，以量化发酵过程中的产量。此手动任务是耗时且容易出错的。在这项工作中，我们根据U-NET CNN体系结构测试多个分割算法，并表明这些算法提供了强大的自动化CFU计数。我们表明，具有定制损失函数的多类概括允许以可接受的准确性来区分有毒和活泼的菌落。尽管要探索许多可能性，但我们的结果表明了深度学习的潜力，可以分离和分类细菌菌落。
During the development of vaccines, bacterial colony forming units (CFUs) are counted in order to quantify the yield in the fermentation process. This manual task is time-consuming and error-prone. In this work we test multiple segmentation algorithms based on the U-Net CNN architecture and show that these offer robust, automated CFU counting. We show that the multiclass generalisation with a bespoke loss function allows distinguishing virulent and avirulent colonies with acceptable accuracy. While many possibilities are left to explore, our results show the potential of deep learning for separating and classifying bacterial colonies.
