# High-throughput molecular imaging via deep learning enabled Raman spectroscopy

arXiv: https://arxiv.org/abs/2009.13318 · HTML: https://mlsanity.com/paper/2009.13318/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2020.

## Built on by (most cited first, 9 of 9)

- [When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development](https://mlsanity.com/paper/2209.01083/) (2022)
- [Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders](https://mlsanity.com/paper/2403.04526/) (2024)
- [Integration of Programmable Diffraction with Digital Neural Networks](https://mlsanity.com/paper/2406.10688/) (2024)
- [A Self-supervised Learning Method for Raman Spectroscopy based on Masked Autoencoders](https://mlsanity.com/paper/2504.16130/) (2025)
- [RamanPFN: learning from Raman spectral structure with a tabular foundation model](https://mlsanity.com/paper/2608.02157/) (2026)
- [spectrai: A deep learning framework for spectral data](https://mlsanity.com/paper/2108.07595/) (2021)
- [OASIS: A Deep Learning Framework for Universal Spectroscopic Analysis Driven by Novel Loss Functions](https://mlsanity.com/paper/2509.11499/) (2025)
- [Machine Learning Enhanced Laser Spectroscopy for Multi-Species Gas Detection in Complex and Harsh Environments](https://mlsanity.com/paper/2605.01306/) (2026)
- [A Practical Noise2Noise Denoising Pipeline for High-Throughput Raman Spectroscopy](https://mlsanity.com/paper/2605.18511/) (2026)

## Abstract (arXiv)

Raman spectroscopy enables non-destructive, label-free imaging with unprecedented molecular contrast but is limited by slow data acquisition, largely preventing high-throughput imaging applications. Here, we present a comprehensive framework for higher-throughput molecular imaging via deep learning enabled Raman spectroscopy, termed DeepeR, trained on a large dataset of hyperspectral Raman images, with over 1.5 million spectra (400 hours of acquisition) in total. We firstly perform denoising and reconstruction of low signal-to-noise ratio Raman molecular signatures via deep learning, with a 9x improvement in mean squared error over state-of-the-art Raman filtering methods. Next, we develop a neural network for robust 2-4x super-resolution of hyperspectral Raman images that preserves molecular cellular information. Combining these approaches, we achieve Raman imaging speed-ups of up to 160x, enabling high resolution, high signal-to-noise ratio cellular imaging in under one minute. Finally, transfer learning is applied to extend DeepeR from cell to tissue-scale imaging. DeepeR provides a foundation that will enable a host of higher-throughput Raman spectroscopy and molecular imaging applications across biomedicine.