  A substantial body of research has focused on developing systems that assist
medical professionals during labor-intensive early screening processes, many
based on convolutional deep-learning architectures. Recently, multiple studies
explored the application of so-called self-attention mechanisms in the vision
domain. These studies often report empirical improvements over fully
convolutional approaches on various datasets and tasks. To evaluate this trend
for medical imaging, we extend two widely adopted convolutional architectures
with different self-attention variants on two different medical datasets. With
this, we aim to specifically evaluate the possible advantages of additional
self-attention. We compare our models with similarly sized convolutional and
attention-based baselines and evaluate performance gains statistically.
Additionally, we investigate how including such layers changes the features
learned by these models during the training. Following a hyperparameter search,
and contrary to our expectations, we observe no significant improvement in
balanced accuracy over fully convolutional models. We also find that important
features, such as dermoscopic structures in skin lesion images, are still not
learned by employing self-attention. Finally, analyzing local explanations, we
confirm biased feature usage. We conclude that merely incorporating attention
is insufficient to surpass the performance of existing fully convolutional
methods.
