Shape and Margin-Aware Lung Nodule Classification in Low-dose CT Images via Soft Activation Mapping

TL;DR

This paper introduces a novel soft activation mapping (SAM) and a high-level feature enhancement scheme (HESAM) to improve lung nodule classification in low-dose CT images by capturing fine-grained shape and margin features, enhancing interpretability and accuracy.

Contribution

The paper proposes SAM and HESAM methods that better capture and localize fine-grained nodule features, surpassing existing interpretability techniques in lung nodule classification.

Findings

SAM captures more detailed attention regions than existing methods.

HESAM localizes features more accurately and improves predictive performance.

The approach reduces false positives and aligns with radiologists' insights.

Abstract

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.