# Classification of crystallization outcomes using deep convolutional neural networks

arXiv: https://arxiv.org/abs/1803.10342 · HTML: https://mlsanity.com/paper/1803.10342/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2018.

## Built on by (most cited first, 4 of 4)

- [MUFASA: Multimodal Fusion Architecture Search for Electronic Health Records](https://mlsanity.com/paper/2102.02340/) (2021)
- [Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey](https://mlsanity.com/paper/2209.11123/) (2022)
- [Convolutional Neural Networks: Basic Concepts and Applications in Manufacturing](https://mlsanity.com/paper/2210.07848/) (2022)
- [Protein Crystallization Segmentation and Classification Using Subordinate Color Channel in Fluorescence Microscopy Images](https://mlsanity.com/paper/2610.15286/) (2026)

## Abstract (arXiv)

The Machine Recognition of Crystallization Outcomes (MARCO) initiative has assembled roughly half a million annotated images of macromolecular crystallization experiments from various sources and setups. Here, state-of-the-art machine learning algorithms are trained and tested on different parts of this data set. We find that more than 94% of the test images can be correctly labeled, irrespective of their experimental origin. Because crystal recognition is key to high-density screening and the systematic analysis of crystallization experiments, this approach opens the door to both industrial and fundamental research applications.