About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
|
AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
|
Presentation Title |
Weakly-Supervised Segmentation of Microstructure Images with Deep Convolutional Neural Networks |
Author(s) |
Bo Lei, Elizabeth A. Holm |
On-Site Speaker (Planned) |
Elizabeth A. Holm |
Abstract Scope |
Deep convolutional neural networks have demonstrated outstanding predictive capability in complicated microstructure segmentation tasks. However, typical deep learning solutions require a considerable amount of dense pixel-level annotations for model development. These annotations are especially difficult and time-consuming to obtain for materials images. Here, we focus on a weakly-supervised method that only uses hundreds of pixel annotations per image and reaches comparable performance to fully-supervised method. The method is developed in an active learning manner where pixel annotations are queried from users incrementally, making it possible to be integrated with interactive programs. This approach can significantly reduce the annotation efforts and would mostly benefits rapid microstructure characterization in common materials research and development scenarios. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Characterization, Computational Materials Science & Engineering |