  To increase the transparency of modern computer-aided diagnosis (CAD) systems
for assessing the malignancy of lung nodules, an interpretable model based on
applying the generalized additive models and the concept-based learning is
proposed. The model detects a set of clinically significant attributes in
addition to the final malignancy regression score and learns the association
between the lung nodule attributes and a final diagnosis decision as well as
their contributions into the decision. The proposed concept-based learning
framework provides human-readable explanations in terms of different concepts
(numerical and categorical), their values, and their contribution to the final
prediction. Numerical experiments with the LIDC-IDRI dataset demonstrate that
the diagnosis results obtained using the proposed model, which explicitly
explores internal relationships, are in line with similar patterns observed in
clinical practice. Additionally, the proposed model shows the competitive
classification and the nodule attribute scoring performance, highlighting its
potential for effective decision-making in the lung nodule diagnosis.
