  Clinical decision support using deep neural networks has become a topic of
steadily growing interest. While recent work has repeatedly demonstrated that
deep learning offers major advantages for medical image classification over
traditional methods, clinicians are often hesitant to adopt the technology
because its underlying decision-making process is considered to be
intransparent and difficult to comprehend. In recent years, this has been
addressed by a variety of approaches that have successfully contributed to
providing deeper insight. Most notably, additive feature attribution methods
are able to propagate decisions back into the input space by creating a
saliency map which allows the practitioner to "see what the network sees."
However, the quality of the generated maps can become poor and the images noisy
if only limited data is available - a typical scenario in clinical contexts. We
propose a novel decision explanation scheme based on CycleGAN activation
maximization which generates high-quality visualizations of classifier
decisions even in smaller data sets. We conducted a user study in which we
evaluated our method on the LIDC dataset for lung lesion malignancy
classification, the BreastMNIST dataset for ultrasound image breast cancer
detection, as well as two subsets of the CIFAR-10 dataset for RBG image object
recognition. Within this user study, our method clearly outperformed existing
approaches on the medical imaging datasets and ranked second in the natural
image setting. With our approach we make a significant contribution towards a
better understanding of clinical decision support systems based on deep neural
networks and thus aim to foster overall clinical acceptance.
