Interpretative Computer-aided Lung Cancer Diagnosis: from Radiology Analysis to Malignancy Evaluation

TL;DR

This paper introduces R2MNet, a deep learning model that combines radiology feature analysis with malignancy evaluation, improving interpretability and accuracy in lung cancer diagnosis from CT scans.

Contribution

The paper presents a joint radiology analysis and malignancy evaluation network with channel-dependent activation mapping for interpretability, advancing clinical relevance of deep learning models.

Findings

Achieved 96.27% AUC in radiology analysis

Achieved 97.52% AUC in malignancy evaluation

Model explanations align with radiologists' diagnostic cues

Abstract

Background and Objective:Computer-aided diagnosis (CAD) systems promote diagnosis effectiveness and alleviate pressure of radiologists. A CAD system for lung cancer diagnosis includes nodule candidate detection and nodule malignancy evaluation. Recently, deep learning-based pulmonary nodule detection has reached satisfactory performance ready for clinical application. However, deep learning-based nodule malignancy evaluation depends on heuristic inference from low-dose computed tomography volume to malignant probability, which lacks clinical cognition. Methods:In this paper, we propose a joint radiology analysis and malignancy evaluation network (R2MNet) to evaluate the pulmonary nodule malignancy via radiology characteristics analysis. Radiological features are extracted as channel descriptor to highlight specific regions of the input volume that are critical for nodule malignancy evaluation. In addition, for model explanations, we propose channel-dependent activation mapping to visualize the features and shed light on the decision process of deep neural network. Results:Experimental results on the LIDC-IDRI dataset demonstrate that the proposed method achieved area under curve of 96.27% on nodule radiology analysis and AUC of 97.52% on nodule malignancy evaluation. In addition, explanations of CDAM features proved that the shape and density of nodule regions were two critical factors that influence a nodule to be inferred as malignant, which conforms with the diagnosis cognition of experienced radiologists. Conclusion:Incorporating radiology analysis with nodule malignant evaluation, the network inference process conforms to the diagnostic procedure of radiologists and increases the confidence of evaluation results. Besides, model interpretation with CDAM features shed light on the regions which DNNs focus on when they estimate nodule malignancy probabilities.