|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
||Investigation of Microstructure Image Segmentation via Deep Learning with Limited Data Annotations
||Bo Lei, Elizabeth Holm
|On-Site Speaker (Planned)
In quantitative microscopy, microstructure image segmentation is essential for image analysis and materials characterization. The rising deep convolutional neural network methods for semantic segmentation in natural images have recently been transferred to materials images and demonstrated outstanding performance in complicated microstructure datasets. However, typical deep learning solutions require a considerable amount of data annotations to reach good performance and they are especially difficult to obtain for materials images. Here, we investigated the possibility to significantly reduce the amount of human annotations while achieving comparable results with the help of transfer learning and data augmentation. We introduced bag-of-words method for training image selection and made adjustments to the deep network model for better generality.
||Machine Learning, Computational Materials Science & Engineering, Characterization