Emerging artificial intelligence methods for fighting lung cancer: A survey

Abstract

Lung cancer has one of the highest incidence rates and mortality rates among all common cancers worldwide. Early detection of suspicious lung nodules is crucial in fighting lung cancer. In recent years, with the proliferation of clinical data like low-dose computed tomography (LDCT), histology whole slide images, electronic health records, and sensor readings from medical IoT devices etc., many artificial intelligence tools have taken more important roles in lung cancer management. In this survey, we lay out the current and emergent artificial intelligence methods for fighting lung cancers. Besides the commonly used CT image based deep learning models for detecting and diagnosing lung nodules, we also cover emergent AI techniques for lung cancer: 1) federated deep learning models for harnessing multi-center data with privacy in mind, 2) multi-modal deep learning models for integrating multiple sources of clinical and image data, 3) interpretable deep learning models for opening the black box for clinicians. In the big data era for cancer management, we believe this short survey will help AI researchers better understand the clinical challenges of lung cancer and will also help clinicians better understand the emergent AI tools.