  Lung cancer is a leading cause of death worldwide and early screening is
critical for improving survival outcomes. In clinical practice, the contextual
structure of nodules and the accumulated experience of radiologists are the two
core elements related to the accuracy of identification of benign and malignant
nodules. Contextual information provides comprehensive information about
nodules such as location, shape, and peripheral vessels, and experienced
radiologists can search for clues from previous cases as a reference to enrich
the basis of decision-making. In this paper, we propose a radiologist-inspired
method to simulate the diagnostic process of radiologists, which is composed of
context parsing and prototype recalling modules. The context parsing module
first segments the context structure of nodules and then aggregates contextual
information for a more comprehensive understanding of the nodule. The prototype
recalling module utilizes prototype-based learning to condense previously
learned cases as prototypes for comparative analysis, which is updated online
in a momentum way during training. Building on the two modules, our method
leverages both the intrinsic characteristics of the nodules and the external
knowledge accumulated from other nodules to achieve a sound diagnosis. To meet
the needs of both low-dose and noncontrast screening, we collect a large-scale
dataset of 12,852 and 4,029 nodules from low-dose and noncontrast CTs
respectively, each with pathology- or follow-up-confirmed labels. Experiments
on several datasets demonstrate that our method achieves advanced screening
performance on both low-dose and noncontrast scenarios.
