A framework for general-purpose microscopic image analysis via self-supervised learning

Zhiwei Zheng, Xuezheng Yue, Jincheng Wang, Juan Hou

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Combining materials science, artificial intelligence (AI) offers great potential for the extensive quantitative analysis and processing of material characterization associated with high-throughput experiments. However, due to the complex and diverse morphology of structural components, the development of AI models requires substantial annotation and models are usually task-specific with limited generalizability to different clinical applications. Here, we present a universal self-supervised learning framework for microscopic images. Our framework learns generalizable representations from unlabelled images and provides a pixel-wise segmentation for quantitative microstructure analysis in a variety of materials science applications. Specifically, the framework learns feature from a single image by means of self-supervised learning, and adapts it to a series of related tasks. We show that our method consistently outperforms several comparisons supervised or weakly supervised learning models in the context of various applications. Our approach provides a generalizable solution to improve model performance and alleviate the annotation workload of experts to enable practical AI applications from microscopic imaging.

Original languageEnglish
Article number114003
Number of pages13
JournalMaterials Characterization
Volume213
Early online date1 Jun 2024
DOIs
Publication statusPublished - Jul 2024

Fingerprint

Dive into the research topics of 'A framework for general-purpose microscopic image analysis via self-supervised learning'. Together they form a unique fingerprint.

Cite this