# Roadmap on Deep Learning for Microscopy

arXiv: https://arxiv.org/abs/2303.03793 · HTML: https://mlsanity.com/paper/2303.03793/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2023.

## Built on by (most cited first, 6 of 6)

- [Learned, uncertainty-driven adaptive acquisition for photon-efficient scanning microscopy](https://mlsanity.com/paper/2310.16102/) (2023)
- [Local Conditional Neural Fields for Versatile and Generalizable Large-Scale Reconstructions in Computational Imaging](https://mlsanity.com/paper/2307.06207/) (2023)
- [AI-based analysis of super-resolution microscopy: Biological discovery in the absence of ground truth](https://mlsanity.com/paper/2305.17193/) (2023)
- [WaveOrder: A differentiable wave-optical framework for scalable biological microscopy with diverse modalities](https://mlsanity.com/paper/2412.09775/) (2024)
- [Metric-Guided Synthetic Image Data Rendering for Deep Learning compatible with Agentic AI](https://mlsanity.com/paper/2607.12874/) (2026)
- [CARE: A Lightweight Plug-in Gated Correction and Uncertainty-aware Module for Long-term Time Series Forecasting](https://mlsanity.com/paper/2610.11165/) (2026)

## Abstract (arXiv)

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences.