Skip To Main Content
Inbal Cohen on Muck Rack

Inbal Cohen

As seen in: Nature

Get in touch with Inbal

Contact Inbal, search articles and posts on X, monitor coverage, and track replies from one place.

Learn more about Muck Rack

Actions

Share this page

Articles

Measurement-aware learning for reliable grain-boundary analysis in quantitative metallography

|
By Boaz Meivar, Inbal Cohen, Julien Robitaille, Shai Avidan, Gal Oren, Matan Rusanovsky, Francis Quintal Lauzon, Ofer Be’eri
| Nature Verified
Abstract Quantitative metallography requires reliable boundary-derived measurement. Prior computational pipelines established feasibility, but reliability gaps remain when weak and interrupted interfaces are analyzed under crop-based workflows. Here, we study how context availability, annotation-aligned supervision, and measurement-oriented validation affect boundary-derived grain-size analysis.
Open in Who Shared Issue with article?
See all 1 articles