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Tuberculosis (TB) remains a global health problem, and is the leading causeof death from an infectious disease. A crucial step in the treatment oftuberculosis is screening high risk populations and the early detection of thedisease, with chest x-ray (CXR) imaging being the most widely-used imagingmodality. As such, there has been significant recent interest in artificialintelligence-based TB screening solutions for use in resource-limited scenarioswhere there is a lack of trained healthcare workers with expertise in CXRinterpretation. Motivated by this pressing need and the recent recommendationby the World Health Organization (WHO) for the use of computer-aided diagnosisof TB, we introduce TB-Net, a self-attention deep convolutional neural networktailored for TB case screening. More specifically, we leveraged machine-drivendesign exploration to build a highly customized deep neural networkarchitecture with attention condensers. We conducted an explainability-drivenperformance validation process to validate TB-Net's decision-making behaviour.Experiments on CXR data from a multi-national patient cohort showed that theproposed TB-Net is able to achieve accuracy/sensitivity/specificity of99.86%/100.0%/99.71%. Radiologist validation was conducted on select cases bytwo board-certified radiologists with over 10 and 19 years of experience,respectively, and showed consistency between radiologist interpretation andcritical factors leveraged by TB-Net for TB case detection for the case whereradiologists identified anomalies. While not a production-ready solution, wehope that the open-source release of TB-Net as part of the COVID-Net initiativewill support researchers, clinicians, and citizen data scientists in advancingthis field in the fight against this global public health crisis.

TB-Net: A Tailored, Self-Attention Deep Convolutional Neural Network Design for Detection of Tuberculosis Cases from Chest X-ray Images