Deep representation learning for domain adaptable classification of infrared spectral imaging data

Area of Science:

• Biomedical Imaging
• Computational Pathology
• Spectroscopy

Background:

• Infrared microscopy is crucial for tissue diagnostics, but relies on computationally intensive preprocessing of pixel spectra.
• Current methods use non-linear physical models, leading to poor generalization across tissue preparations and slow processing times.
• This limits the clinical application of advanced infrared microscopy techniques.

Purpose of the Study:

• To develop a computationally efficient and robust preprocessing method for infrared microscopic pixel spectra.
• To improve the generalization capabilities of tissue classifiers across different sample preparations.
• To enable faster and more reliable tissue analysis using infrared spectral imaging.

Main Methods:

• Applied stacked contractive autoencoders (SCAE) for unsupervised preprocessing of infrared pixel spectra.
• Utilized supervised fine-tuning to train neural networks for tissue structure resolution.
• Validated classifier robustness by transferring a network trained on embedded tissue to fresh frozen tissue.

Main Results:

• Unsupervised pretraining with SCAE enabled features to generalize across significant spectral differences between embedded and fresh frozen tissue.
• Achieved reliable tissue structure resolution and classification.
• Demonstrated successful transfer learning, eliminating the need for training separate classifiers from scratch.

Conclusions:

• Stacked contractive autoencoders offer a powerful unsupervised approach for infrared spectral image preprocessing.
• This method enhances classifier generalization and significantly reduces computational time.
• The approach facilitates the integration of rapid infrared microscopy into clinical tissue diagnostics.