  Feature-based self-explanatory methods explain their classification in terms
of human-understandable features. In the medical imaging community, this
semantic matching of clinical knowledge adds significantly to the
trustworthiness of the AI. However, the cost of additional annotation of
features remains a pressing issue. We address this problem by proposing
cRedAnno, a data-/annotation-efficient self-explanatory approach for lung
nodule diagnosis. cRedAnno considerably reduces the annotation need by
introducing self-supervised contrastive learning to alleviate the burden of
learning most parameters from annotation, replacing end-to-end training with
two-stage training. When training with hundreds of nodule samples and only 1%
of their annotations, cRedAnno achieves competitive accuracy in predicting
malignancy, meanwhile significantly surpassing most previous works in
predicting nodule attributes. Visualisation of the learned space further
indicates that the correlation between the clustering of malignancy and nodule
attributes coincides with clinical knowledge. Our complete code is open-source
available: https://github.com/diku-dk/credanno.
