SAM-I-Am: Semantic Boosting for Zero-shot Atomic-Scale Electron Micrograph Segmentation

no code implementations • 9 Apr 2024 • Waqwoya Abebe, Jan Strube, Luanzheng Guo, Nathan R. Tallent, Oceane Bel, Steven Spurgeon, Christina Doty, Ali Jannesari

To enable rapid adaptation of the best segmentation technology, we propose the concept of semantic boosting: given a zero-shot foundation model, guide its segmentation and adjust results to match domain expectations.

Autonomous Driving Image Segmentation +2

Deep Learning for Automated Experimentation in Scanning Transmission Electron Microscopy

no code implementations • 4 Apr 2023 • Sergei V. Kalinin, Debangshu Mukherjee, Kevin M. Roccapriore, Ben Blaiszik, Ayana Ghosh, Maxim A. Ziatdinov, A. Al-Najjar, Christina Doty, Sarah Akers, Nageswara S. Rao, Joshua C. Agar, Steven R. Spurgeon

Machine learning (ML) has become critical for post-acquisition data analysis in (scanning) transmission electron microscopy, (S)TEM, imaging and spectroscopy.

Design of a Graphical User Interface for Few-Shot Machine Learning Classification of Electron Microscopy Data

1 code implementation • 21 Jul 2021 • Christina Doty, Shaun Gallagher, Wenqi Cui, Wenya Chen, Shweta Bhushan, Marjolein Oostrom, Sarah Akers, Steven R. Spurgeon

The recent growth in data volumes produced by modern electron microscopes requires rapid, scalable, and flexible approaches to image segmentation and analysis.

Few-Shot Learning Image Segmentation +1