  Existing interactive visualization tools for deep learning are mostly applied
to the training, debugging, and refinement of neural network models working on
natural images. However, visual analytics tools are lacking for the specific
application of x-ray image classification with multiple structural attributes.
In this paper, we present an interactive system for domain scientists to
visually study the multiple attributes learning models applied to x-ray
scattering images. It allows domain scientists to interactively explore this
important type of scientific images in embedded spaces that are defined on the
model prediction output, the actual labels, and the discovered feature space of
neural networks. Users are allowed to flexibly select instance images, their
clusters, and compare them regarding the specified visual representation of
attributes. The exploration is guided by the manifestation of model performance
related to mutual relationships among attributes, which often affect the
learning accuracy and effectiveness. The system thus supports domain scientists
to improve the training dataset and model, find questionable attributes labels,
and identify outlier images or spurious data clusters. Case studies and
scientists feedback demonstrate its functionalities and usefulness.
