  The computer-aided detection (CADe) systems are developed to assist
pathologists in slide assessment, increasing diagnosis efficiency and reducing
missing inspections. Many studies have shown such a CADe system with deep
learning approaches outperforms the one using conventional methods that rely on
hand-crafted features based on field-knowledge. However, most developers who
adopted deep learning models directly focused on the efficacy of outcomes,
without providing comprehensive explanations on why their proposed frameworks
can work effectively. In this study, we designed four experiments to verify the
consecutive concepts, showing that the deep features learned from pathological
patches are interpretable by domain knowledge of pathology and enlightening for
clinical diagnosis in the task of lesion detection. The experimental results
show the activation features work as morphological descriptors for specific
cells or tissues, which agree with the clinical rules in classification. That
is, the deep learning framework not only detects the distribution of tumor
cells but also recognizes lymphocytes, collagen fibers, and some other non-cell
structural tissues. Most of the characteristics learned by the deep learning
models have summarized the detection rules that can be recognized by the
experienced pathologists, whereas there are still some features may not be
intuitive to domain experts but discriminative in classification for machines.
Those features are worthy to be further studied in order to find out the
reasonable correlations to pathological knowledge, from which pathological
experts may draw inspirations for exploring new characteristics in diagnosis.
