ArticleApplied spectroscopy2019
Digital Staining of High-Definition Fourier Transform Infrared (FT-IR) Images Using Deep Learning.
Article in Applied spectroscopy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Hardware-Attentive Programmable Fourier Ptychography Enables Task-Adaptive Label-Free Virtual Staining.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- DREAMER-S: Deep leaRning-Enabled Attention-based Multiple-instance approaches with Explainable Representations for Spatial biology.PLoS computational biology · 2026Article
- Enabling Chemical Holography: A Comprehensive Framework and Implementation.IEEE photonics journal · 2025Article
- Single color digital H&E staining with In-and-Out Net.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2024Article
- Cervical Cancer Tissue Analysis Using Photothermal Midinfrared Spectroscopic Imaging.Chemical & biomedical imaging · 2024Article
- Metasurface-enhanced infrared spectroscopy in multiwell format for real-time assaying of live cells.Lab on a chip · 2023Article
- Multi-contrast digital histopathology of mouse organs using quantitative phase imaging and virtual staining.Biomedical optics express · 2023Article
- Article
- Advances in Digital Pathology: From Artificial Intelligence to Label-Free Imaging.Visceral medicine · 2021Review
- Design Considerations for Discrete Frequency Infrared Microscopy Systems.Applied spectroscopy · 2021Article
- Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors.Sensors (Basel, Switzerland) · 2021Review
- Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning.Nature communications · 2019Article
- Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review.Journal of imaging · 2019Review
- Molecular mechanism of antimicrobial activity of chlorhexidine against carbapenem-resistant Acinetobacter baumannii.PloS one · 2019Article
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Authors and funding
4 authors.
Funding
Abstract
Histological stains, such as hematoxylin and eosin (H&E), are routinely used in clinical diagnosis and research. While these labels offer a high degree of specificity, throughput is limited by the need for multiple samples. Traditional histology stains, such as immunohistochemical labels, also rely only on protein expression and cannot quantify small molecules and metabolites that may aid in diagnosis. Finally, chemical stains and dyes permanently alter the tissue, making downstream analysis impossible. Fourier transform infrared (FT-IR) spectroscopic imaging has shown promise for label-free characterization of important tissue phenotypes and can bypass the need for many chemical labels. Fourier transform infrared classification commonly leverages supervised learning, requiring human annotation that is tedious and prone to errors. One alternative is digital staining, which leverages machine learning to map IR spectra to a corresponding chemical stain. This replaces human annotation with computer-aided alignment. Previous work relies on alignment of adjacent serial tissue sections. Since the tissue samples are not identical at the cellular level, this technique cannot be applied to high-definition FT-IR images. In this paper, we demonstrate that cellular-level mapping can be accomplished using identical samples for both FT-IR and chemical labels. In addition, higher-resolution results can be achieved using a deep convolutional neural network that integrates spatial and spectral features.
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Registered trials
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