Visual Understanding of Multiple Attributes Learning Model of X-Ray Scattering Images

The technique is widely used in biomedical, material, and physical applications by analyzing structural patterns in the x-ray scattering images [21]. X-ray equipment can generate up to 1 million images per day which impose heavy burden in post image analysis. A variety of image analysis methods are applied to x-ray scattering data. Recently, deep learning models are employed in classifying and annotating multiple image attributes from experimental or synthetic images, which were shown to outperform previously published methods [18, 4]. As most deep learning paradigms, these methods are not easily understood by material, physical, and biomedical scientists. The lack of proper explanations and absence of control of the decisions would make the models less trustworthy. While considerable effort has been made to make deep learning interpretable and controllable by humans [3], the existing techniques are not specifically designed for the scientific image classification models of x-ray scattering images, which requires extra consideration in finding - How the learning models perform for a diverse set of overlapped attributes with high variation?