User-Guided Interactive Machine Learning for High-Throughput, Multi-Scale Helium Bubble Segmentation and Quantification
收藏DataCite Commons2026-04-02 更新2026-05-05 收录
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https://www.scidb.cn/detail?dataSetId=0e6c92f7383d47bd8765a181d08c79f3
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资源简介：
This project introduces a novel User-Guided Interactive Machine Learning Framework for high-throughput, multi-scale helium bubble segmentation and quantification. Combining advanced image processing techniques with deep learning, the framework leverages Mask R-CNN for semantic segmentation and incorporates GCM for refined analysis. This tool provides a scalable and customizable approach for helium bubble analysis, empowering researchers with precise, user-friendly, and interactive workflows.
提供机构：
Science Data Bank
创建时间：
2026-04-02
