用图神经网络提升电子显微图像分类准确率
EMCNet : Graph-Nets for Electron Micrographs Classification
- 基于图神经网络构建端到端的电子显微图像表征学习框架
- 在多个开源数据集上超越主流基线方法
- 适合材料科学与图像分析领域的研究人员使用
通过电子显微图像对材料进行表征是多个材料加工行业中的重要且具有挑战性的任务。电子显微图像的分类复杂性源于类内差异大、类间相似度高以及图案存在多尺度特征。现有方法在学习复杂图像模式方面表现不佳。本文提出一种基于端到端电子显微图像表征学习的纳米材料识别框架，以应对上述挑战。实验表明，该框架在多个开源纳米材料识别数据集上优于主流基线方法。详细的消融实验验证了所提方法的有效性。
原文摘要 · Abstract (English)
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.
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