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We developed explainable deep learning to enhance mid-infrared photoacoustic microscopy (MIR-PAM) images. This method achieves high-resolution, label-free cellular imaging, overcoming the resolution limitations of traditional MIR-PAM.

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Area of Science:

  • Biomedical Imaging
  • Optical Microscopy
  • Computational Biology

Background:

  • Mid-infrared photoacoustic microscopy (MIR-PAM) offers label-free biochemical information.
  • MIR-PAM's spatial resolution is limited by long optical wavelengths compared to confocal fluorescence microscopy (CFM).
  • Existing methods lack explainability and stability for image transformation.

Purpose of the Study:

  • To develop an explainable deep learning (XDL) framework for transforming low-resolution MIR-PAM images into high-resolution, virtually stained images.
  • To improve the spatial resolution and interpretability of MIR-PAM.
  • To enable label-free, high-resolution cellular imaging.

Main Methods:

  • An unsupervised generative adversarial network (GAN) was employed for inter-domain image transformation.
  • A saliency constraint was integrated into the GAN for enhanced explainability.
  • The XDL framework was validated on cultured human cardiac fibroblasts, comparing results with CFM images.

Main Results:

  • The XDL framework successfully transformed low-resolution MIR-PAM images into high-resolution, confocal-like images.
  • The method accurately identified cell nuclei and filamentous actins.
  • The XDL framework demonstrated stable and reliable performance, ensuring similar saliency between image domains.

Conclusions:

  • Explainable deep learning-based MIR-PAM (XDL-MIR-PAM) enables label-free, high-resolution duplexed cellular imaging.
  • This technique overcomes the spatial resolution limitations of conventional MIR-PAM.
  • XDL-MIR-PAM offers significant benefits for cell biology research by providing detailed, label-free imaging.