A number of studies on lung nodule classification lack clinical/biological
interpretations of the features extracted by convolutional neural network
(CNN). The methods like class activation mapping (CAM) and gradient-based CAM
(Grad-CAM) are tailored for interpreting localization and classification tasks
while they ignored fine-grained features. Therefore, CAM and Grad-CAM cannot
provide optimal interpretation for lung nodule categorization task in low-dose
CT images, in that fine-grained pathological clues like discrete and irregular
shape and margins of nodules are capable of enhancing sensitivity and
specificity of nodule classification with regards to CNN. In this paper, we
first develop a soft activation mapping (SAM) to enable fine-grained lung
nodule shape \& margin (LNSM) feature analysis with a CNN so that it can access
rich discrete features. Secondly, by combining high-level convolutional
features with SAM, we further propose a high-level feature enhancement scheme
(HESAM) to localize LNSM features. Experiments on the LIDC-IDRI dataset
indicate that 1) SAM captures more fine-grained and discrete attention regions
than existing methods, 2) HESAM localizes more accurately on LNSM features and
obtains the state-of-the-art predictive performance, reducing the false
positive rate, and 3) we design and conduct a visually matching experiment
which incorporates radiologists study to increase the confidence level of
applying our method to clinical diagnosis.

论文提出了一种基于卷积神经网络（CNN）的软激活映射（SAM）和高级特征增强方案（HESAM）的肺结节分类方法，能够捕获更细粒度的肺结节形状和边缘特征并实现更精确的本地化，获得了最先进的预测性能，并在医学影像学界取得了实质性进展。