  Detecting malignant pulmonary nodules at an early stage can allow medical
interventions which may increase the survival rate of lung cancer patients.
Using computer vision techniques to detect nodules can improve the sensitivity
and the speed of interpreting chest CT for lung cancer screening. Many studies
have used CNNs to detect nodule candidates. Though such approaches have been
shown to outperform the conventional image processing based methods regarding
the detection accuracy, CNNs are also known to be limited to generalize on
under-represented samples in the training set and prone to imperceptible noise
perturbations. Such limitations can not be easily addressed by scaling up the
dataset or the models. In this work, we propose to add adversarial synthetic
nodules and adversarial attack samples to the training data to improve the
generalization and the robustness of the lung nodule detection systems. To
generate hard examples of nodules from a differentiable nodule synthesizer, we
use projected gradient descent (PGD) to search the latent code within a bounded
neighbourhood that would generate nodules to decrease the detector response. To
make the network more robust to unanticipated noise perturbations, we use PGD
to search for noise patterns that can trigger the network to give
over-confident mistakes. By evaluating on two different benchmark datasets
containing consensus annotations from three radiologists, we show that the
proposed techniques can improve the detection performance on real CT data. To
understand the limitations of both the conventional networks and the proposed
augmented networks, we also perform stress-tests on the false positive
reduction networks by feeding different types of artificially produced patches.
We show that the augmented networks are more robust to both under-represented
nodules as well as resistant to noise perturbations.
