  Explanations for deep neural network predictions in terms of domain-related
concepts can be valuable in medical applications, where justifications are
important for confidence in the decision-making. In this work, we propose a
methodology to exploit continuous concept measures as Regression Concept
Vectors (RCVs) in the activation space of a layer. The directional derivative
of the decision function along the RCVs represents the network sensitivity to
increasing values of a given concept measure. When applied to breast cancer
grading, nuclei texture emerges as a relevant concept in the detection of tumor
tissue in breast lymph node samples. We evaluate score robustness and
consistency by statistical analysis.
