Lung cancer is the leading cause of cancer-related mortality, with small cell lung cancer and non-small cell lung cancer being the primary subtypes that exhibit distinct treatment approaches and prognostic outcomes. Accurate identification of these lung cancer classes holds significant importance in clinical practice. This study introduces an experience-driven interpretable multi-task network to concurrently perform segmentation and classification of small cell and non-small cell lung cancer. The core architecture of this multi-task model is based on StarNet, featuring a shared feature extraction branch and task-specific decoding branches for tumor segmentation and classification. Leveraging clinical knowledge of small cell lung cancer characteristics, such as indistinct edges, tissue invasion, and limited large cavity areas, two auxiliary branches are proposed: edge uncertainty estimation and tumor core area reconstruction. The values from edge uncertainty estimation and reconstruction integrity estimation are utilized in the classification branch to facilitate small cell lung cancer classification. Furthermore, for enhanced interpretability, bottleneck layer features are extracted for comparative learning, and a three-level contrastive loss is proposed to improve the differentiation of disease features. Lastly, an interpretable strategy based on trained feature query matching is presented, providing radiologists with clinical insights and reference images while the model outputs recognition predictions. Experimental results on the public dataset demonstrate that the proposed multi-task model not only outperforms single-task models but also offers a certain level of interpretability, thus enhancing radiologists' clinical decision-making processes.
Diao et al. (Thu,) studied this question.