  Although deep learning algorithms have been intensively developed for
computer-aided tuberculosis diagnosis (CTD), they mainly depend on carefully
annotated datasets, leading to much time and resource consumption. Weakly
supervised learning (WSL), which leverages coarse-grained labels to accomplish
fine-grained tasks, has the potential to solve this problem. In this paper, we
first propose a new large-scale tuberculosis (TB) chest X-ray dataset, namely
the tuberculosis chest X-ray attribute dataset (TBX-Att), and then establish an
attribute-assisted weakly-supervised framework to classify and localize TB by
leveraging the attribute information to overcome the insufficiency of
supervision in WSL scenarios. Specifically, first, the TBX-Att dataset contains
2000 X-ray images with seven kinds of attributes for TB relational reasoning,
which are annotated by experienced radiologists. It also includes the public
TBX11K dataset with 11200 X-ray images to facilitate weakly supervised
detection. Second, we exploit a multi-scale feature interaction model for TB
area classification and detection with attribute relational reasoning. The
proposed model is evaluated on the TBX-Att dataset and will serve as a solid
baseline for future research. The code and data will be available at
https://github.com/GangmingZhao/tb-attribute-weak-localization.
