  Lung nodules can be an alarming precursor to potential lung cancer. Missed
nodule detections during chest radiograph analysis remains a common challenge
among thoracic radiologists. In this work, we present a multi-task lung nodule
detection algorithm for chest radiograph analysis. Unlike past approaches, our
algorithm predicts a global-level label indicating nodule presence along with
local-level labels predicting nodule locations using a Dual Head Network (DHN).
We demonstrate the favorable nodule detection performance that our multi-task
formulation yields in comparison to conventional methods. In addition, we
introduce a novel Dual Head Augmentation (DHA) strategy tailored for DHN, and
we demonstrate its significance in further enhancing global and local nodule
predictions.
