EMCNet : Graph-Nets for Electron Micrographs Classification
This work addresses a domain-specific problem in materials processing industries, but it appears incremental as it builds on existing graph-net methods for a specific application.
The paper tackled the challenging task of classifying electron micrographs for nanomaterial identification, which suffers from high intra-class dissimilarity and inter-class similarity, and reported that their framework outperforms popular baselines on open-source datasets.
Characterization of materials via electron micrographs is an important and challenging task in several materials processing industries. Classification of electron micrographs is complex due to the high intra-class dissimilarity, high inter-class similarity, and multi-spatial scales of patterns. However, existing methods are ineffective in learning complex image patterns. We propose an effective end-to-end electron micrograph representation learning-based framework for nanomaterial identification to overcome the challenges. We demonstrate that our framework outperforms the popular baselines on the open-source datasets in nanomaterials-based identification tasks. The ablation studies are reported in great detail to support the efficacy of our approach.