用贝叶斯网络提升材料辐照微观结构分割精度，增强对氚行为预测的解释力。
Bayesian SegNet for Semantic Segmentation with Improved Interpretation of Microstructural Evolution During Irradiation of Materials
- 引入贝叶斯框架与元数据，提升模型对辐照微结构变化的敏感度。
- 在缺陷面积、密度等指标上，模型分割结果与专家标注高度一致。
- 适合材料科学中需要可解释性分割的辐照实验分析场景。
理解辐照LiAlO2颗粒中微观结构演变与氚扩散、滞留及释放之间的关系，有助于提升氚生成可燃吸收棒性能预测。基于专家标注的辐照与未辐照样品图像，我们训练深度卷积神经网络将图像分割为缺陷、晶粒和边界三类。从分割结果中提取定性微结构信息，用于比较辐照前后样品差异。通过引入元数据和不确定性量化等改进策略，提升了模型敏感性。在像素比例、缺陷面积和缺陷密度等微结构表征指标上，模型预测结果与专家标注基本一致。对于辐照与未辐照图像，最佳模型均展现出高精度性能，表明神经网络可作为专家标注图像的可行替代方案。
原文摘要 · Abstract (English)
Understanding the relationship between the evolution of microstructures of irradiated LiAlO2 pellets and tritium diffusion, retention and release could improve predictions of tritium-producing burnable absorber rod performance. Given expert-labeled segmented images of irradiated and unirradiated pellets, we trained Deep Convolutional Neural Networks to segment images into defect, grain, and boundary classes. Qualitative microstructural information was calculated from these segmented images to facilitate the comparison of unirradiated and irradiated pellets. We tested modifications to improve the sensitivity of the model, including incorporating meta-data into the model and utilizing uncertainty quantification. The predicted segmentation was similar to the expert-labeled segmentation for most methods of microstructural qualification, including pixel proportion, defect area, and defect density. Overall, the high performance metrics for the best models for both irradiated and unirradiated images shows that utilizing neural network models is a viable alternative to expert-labeled images.
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