MS-Net: Learning to assess the malignant status of a lung nodule by a radiologist and her peers.

Background: Automatically assessing the malignant status of lung nodules based on CTscan images can help reduce the workload of radiologists while improving their diagnostic accuracy.

Purpose: Despite remarkable progress in the automatic diagnosis of pulmonary nodules by deep learning technologies, two significant problems remain outstanding. First, end-to-end deep learning solutions tend to neglect the empirical (semantic) features accumulated by radiologists and only rely on automatic features discovered by neural networks to provide the final diagnostic results, leading to questionable reliability, and interpretability. Second, inconsistent diagnosis between radiologists, a widely acknowledged phenomenon in clinical settings, is rarely examined and quantitatively explored by existing machine learning approaches. This paper solves these problems.

Methods: We propose a novel deep neural network called MS-Net, which comprises two sequential modules: A feature derivation and initial diagnosis module (FDID), followed by a diagnosis refinement module (DR). Specifically, to take advantage of accumulated empirical features and discovered automatic features, the FDID model of MS-Net first derives a range of perceptible features and provides two initial diagnoses for lung nodules; then, these results are fed to the subsequent DR module to refine the diagnoses further. In addition, to fully consider the individual and panel diagnosis opinions, we propose a new loss function called collaborative loss, which can collaboratively optimize the individual and her peers' opinions to provide a more accurate diagnosis.

Results: We evaluate the performance of the proposed MS-Net on the Lung Image Database Consortium image collection (LIDC-IDRI). It achieves 92.4% of accuracy, 92.9% of sensitivity, and 92.0% of specificity when panel labels are the ground truth, which is superior to other state-of-the-art diagnosis models. As a byproduct, the MS-Net can automatically derive a range of semantic features of lung nodules, increasing the interpretability of the final diagnoses.

Conclusions: The proposed MS-Net can provide an automatic and accurate diagnosis of lung nodules, meeting the need for a reliable computer-aided diagnosis system in clinical practice.