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Updated: Oct 23, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

DECIMER 1.0: deep learning for chemical image recognition using transformers.

Kohulan Rajan¹, Achim Zielesny², Christoph Steinbeck³

¹Institute for Inorganic and Analytical Chemistry, Friedrich-Schiller-University Jena, Lessingstr. 8, 07743, Jena, Germany.

Journal of Cheminformatics

|August 18, 2021

Summary

This summary is machine-generated.

Researchers developed DECIMER, a deep learning tool, to automatically convert chemical structure images into computer-readable data. This advanced Optical Chemical Structure Recognition (OCSR) achieves over 96% accuracy, significantly improving chemical data accessibility.

Keywords:

Chemical data extraction Deep learning Neural networks Optical chemical structure recognition

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Published on: March 13, 2021

Area of Science:

Computational chemistry
Cheminformatics
Artificial intelligence in chemistry

Background:

Vast amounts of chemical literature, especially pre-1990s, exist in non-digital formats.
Manual extraction of chemical structure data from these sources is time-consuming and prone to errors.
Optical Chemical Structure Recognition (OCSR) tools are needed for automated data extraction.

Purpose of the Study:

To develop an automated, open-source software solution for Optical Chemical Structure Recognition (OCSR).
To leverage deep learning for accurate conversion of chemical structure images into computer-readable formats.
To improve the accessibility and usability of historical chemical data.

Main Methods:

Exploration of various deep learning approaches for OCSR.
Development of a novel transformer-based neural network model named DECIMER.
Training and evaluation of the DECIMER model on chemical structure depictions.

Main Results:

The DECIMER model achieves over 96% accuracy in predicting SMILES from chemical structure depictions lacking stereochemistry.
The model demonstrates over 89% accuracy for depictions including stereochemical information.
This represents a significant advancement over existing rule-based OCSR tools.

Conclusions:

DECIMER offers a highly accurate and automated solution for extracting chemical structure information from images.
The developed model facilitates the digitization and accessibility of historical chemical data.
This work advances the application of deep learning in cheminformatics and chemical data management.