BODY: The success of deep learning, the implementation of lung cancer screening, and recent public challenges have created a revived interest for research of automatic nodule detection and characterization algorithms. In this talk, I will give an overview of deep learning for nodule detection and characterization. Public challenges, such as the LUNA16 challenge and the Kaggle Data Science Bowl 2017 challenge, and publicly available databases have been drivers for development of novel deep learning algorithms and the performance of these novel algorithms is very promising. However, there are remaining challenges that need to be solved. Almost all approaches still rely on only one scan, while a radiologist typically looks at all available scans and the clinical history of the patient. Next to this, integration of these algorithms into clinical practice is still not easy, and validation of algorithms on larger datasets and real-world data are needed to further validate these algorithms. TAKE HOME POINTS: 1) LUNA16 and Kaggle Data Science Bowl 2017 have shown the potential for deep learning for nodule detection and deep learning. 2) Integration of these algorithms into clinical practice is still suboptimal. 3) Validation of these algorithms on larger and modern datasets is needed to further validate these algorithms.
Deep learning for detection and characterization of lung nodules
C. Jacobs and B. van Ginneken
Annual Meeting of the European Society of Thoracic Imaging 2019.