Enhancing Open-World Bacterial Raman Spectra Identification by Feature Regularization for Improved Resilience against...

Area of Science:

• Microbiology
• Spectroscopy
• Artificial Intelligence

Background:

• Deep learning and Raman spectroscopy offer rapid bacterial identification in clinical settings.
• Traditional closed-set models fail with unknown or emerging pathogens, leading to high false positive rates.
• Current neural networks are vulnerable to unpredictable clinical environments and unknown microbial inputs.

Purpose of the Study:

• To develop a robust deep learning model for accurate and reliable pathogen identification using Raman spectroscopy.
• To address the limitations of closed-set classification by effectively handling unknown bacterial samples.
• To reduce the false positive rate in pathogen detection and improve adaptability to emerging microbial threats.

Main Methods:

• Developed an ensemble of ResNet architectures incorporating an attention mechanism.
• Integrated feature regularization using the Objectosphere loss function for improved classification.
• Evaluated the model's performance in identifying known pathogens and detecting unknown samples.

Main Results:

• Achieved a 30-isolate accuracy of 87.8 ± 0.1% for known pathogen identification.
• Effectively separated unknown samples, significantly reducing the false positive rate.
• Demonstrated enhanced performance of out-of-distribution detectors for improved unknown class detection.

Conclusions:

• The developed algorithm enhances the identification of known and unknown pathogens via Raman spectroscopy.
• The method ensures adaptability to future emerging pathogens, increasing diagnostic reliability.
• The approach can be extended to improve open-set medical image classification in dynamic settings.