.png){kind=icon}
返回
A framework for general-purpose microscopic image analysis via self-supervised learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.matchar.2024.114003.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Quantitative microscopic analysis
Electron microscope
Optical microscope
Unsupervised learning
Image segmentation
期刊
IF:
5.5
论文数:
1.1W
被引数:
3.4W
机构
引用论文
A complete representation of structure-property relationships in crystals.png)
NATURE MATERIALS
NATURE MATERIALSIF38.5
PREAI
分享收藏Big-deep-smart data in imaging for guiding materials design大-深-智能成像数据指导材料设计NATURE MATERIALS
OAAI
NATURE MATERIALSIF38.5
OAAI分享收藏
查看更多内容