  As the COVID-19 pandemic aggravated the excessive workload of doctors
globally, the demand for computer aided methods in medical imaging analysis
increased even further. Such tools can result in more robust diagnostic
pipelines which are less prone to human errors. In our paper, we present a deep
neural network to which we refer to as Attention BCDU-Net, and apply it to the
task of lung and heart segmentation from chest X-ray (CXR) images, a basic but
ardous step in the diagnostic pipeline, for instance for the detection of
cardiomegaly. We show that the fine-tuned model exceeds previous
state-of-the-art results, reaching $98.1\pm 0.1\%$ Dice score and $95.2\pm
0.1\%$ IoU score on the dataset of Japanese Society of Radiological Technology
(JSRT). Besides that, we demonstrate the relative simplicity of the task by
attaining surprisingly strong results with training sets of size 10 and 20: in
terms of Dice score, $97.0\pm 0.8\%$ and $97.3\pm 0.5$, respectively, while in
terms of IoU score, $92.2\pm 1.2\%$ and $93.3\pm 0.4\%$, respectively. To
achieve these scores, we capitalize on the mixup augmentation technique, which
yields a remarkable gain above $4\%$ IoU score in the size 10 setup.
