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.

翻译：肺癌是全球主要致死病因，早期筛查对改善生存结局至关重要。临床实践中，结节的上下文结构特征及放射科医师的积累经验是决定良恶性结节识别准确性的两大核心要素。上下文信息提供结节的全面特征，如位置、形态及周围血管，而经验丰富的放射科医师可从既往病例中检索线索作为参考，以充实决策依据。本文提出一种受放射科医师启发的模拟诊断流程方法，包含上下文解析模块与原型回忆模块。上下文解析模块首先分割结节的上下文结构，继而聚合上下文信息以更全面理解结节特征；原型回忆模块采用基于原型的学习方法，将既往学习病例凝练为原型用于比较分析，并通过动量机制在训练中实现原型在线更新。基于这两大模块，本方法同步利用结节内在特征及从其他结节积累的外部知识，实现稳健诊断。为满足低剂量与非增强两种筛查场景需求，我们构建了包含低剂量CT 12,852个结节与非增强CT 4,029个结节的大规模数据集，所有结节均经病理或随访验证标签。多数据集实验结果表明，本方法在低剂量与非增强筛查场景中均达到先进的筛查性能。