Enhancing Open-World Bacterial Raman Spectra Identification by Feature
Regularization for Improved Resilience against Unknown Classes
Abstract
The combination of Deep Learning techniques and Raman spectroscopy shows
great potential offering precise and prompt identification of pathogenic
bacteria in clinical settings. However, the traditional closed-set
classification approaches assume that all test samples belong to one of
the known pathogens, and their applicability is limited since the
clinical environment is inherently unpredictable and dynamic, unknown or
emerging pathogens may not be included in the available catalogs. We
demonstrate that the current state-of-the-art Neural Networks
identifying pathogens through Raman spectra are vulnerable to unknown
inputs, resulting in an uncontrollable false positive rate. To address
this issue, first, we developed a novel ensemble of ResNet architectures
combined with the attention mechanism which outperforms existing
closed-world methods, achieving an accuracy of 87.8±0.1% compared to
the best available modelâ\euro™s accuracy of 86.7 ± 0.4%. Second,
through the integration of feature regularization by the Objectosphere
loss function, our model achieves both high accuracy in identifying
known pathogens from the catalog and effectively separates unknown
samples drastically reducing the false positive rate. Finally, the
proposed feature regularization method during training significantly
enhances the performance of out-ofdistribution detectors during the
inference phase improving the reliability of the detection of unknown
classes. Our novel algorithm for Raman spectroscopy enables the
detection of unknown, uncatalogued, and emerging pathogens providing the
flexibility to adapt to future pathogens that may emerge, and has the
potential to improve the reliability of Raman-based solutions in dynamic
operating environments where accuracy is critical, such as public safety
applications.Â