  Tuberculosis (TB) remains a global health problem, and is the leading cause
of death from an infectious disease. A crucial step in the treatment of
tuberculosis is screening high risk populations and the early detection of the
disease, with chest x-ray (CXR) imaging being the most widely-used imaging
modality. As such, there has been significant recent interest in artificial
intelligence-based TB screening solutions for use in resource-limited scenarios
where there is a lack of trained healthcare workers with expertise in CXR
interpretation. Motivated by this pressing need and the recent recommendation
by the World Health Organization (WHO) for the use of computer-aided diagnosis
of TB, we introduce TB-Net, a self-attention deep convolutional neural network
tailored for TB case screening. More specifically, we leveraged machine-driven
design exploration to build a highly customized deep neural network
architecture with attention condensers. We conducted an explainability-driven
performance validation process to validate TB-Net's decision-making behaviour.
Experiments on CXR data from a multi-national patient cohort showed that the
proposed TB-Net is able to achieve accuracy/sensitivity/specificity of
99.86%/100.0%/99.71%. Radiologist validation was conducted on select cases by
two board-certified radiologists with over 10 and 19 years of experience,
respectively, and showed consistency between radiologist interpretation and
critical factors leveraged by TB-Net for TB case detection for the case where
radiologists identified anomalies. While not a production-ready solution, we
hope that the open-source release of TB-Net as part of the COVID-Net initiative
will support researchers, clinicians, and citizen data scientists in advancing
this field in the fight against this global public health crisis.
