Knowing the Unknown: Open-set Bacteria Classification in Gram Stain Microscopic Images

The identification of bacteria species is an important aspect of microbiology, but it can be expensive, laborious and time-consuming. Using computer-aided classification can signifi-cantly reduce the cost and time of diagnosing bacteria subtypes. However, commonly used image recognition models are close-set classification models, and they assume full knowledge of the world. That is, all testing classes are known at training time. Such an assumption cannot be always valid in real-world applications. Open-set recognition models have the ability to both classify known instances and detect unknown samples of novel classes. This work aims to tackle the problem of bacteria subtyping from an open-set perspective. Our framework, OpenGram, combines a convolutional neural network classifier with a Gaussian mixtures model to adapt to open-set classification. The results demonstrate OpenGram's ability to correctly detect unknown bacteria classes that were unseen by the network during training as well as classify known bacteria classes. Our experiments showed that OpenGram was capable of accurately classifying bacteria subtypes under open-set setting at up to 99.33% F1 score and 99.70% AUC.