His report showed that computer vision and image analysis is among the fields that adopted deep learning most successfully. Such success is enabled by computational power, learning algorithm, and data availability. For medical image analysis, however, data and label pose a major challenge. As compared with millions of well-annotated natural images, we usually only have at most a few thousand for medical tasks. Among them, majority has no annotation, few with labels, and fewer of high quality. In this work, we discuss the challenges and possible solutions in applying deep learning methods for pulmonary image analysis when we do not have well-annotated training data. Common tasks are covered including pathological lung segmentation, lobe fissure estimation, airway extraction, and disease pattern classification. For each candidate task, based on its unique features, we present the reason behind the choice of a specific deep learning structure. Further, we discuss the characteristics of pulmonary images, and how to design and generate the training data that is usually constraint by limited clinical resources. Based on the result, we show that for certain tasks in medical image analysis, incomplete label is feasible for training if it sufficiently covers the variance of target subject. In the end of the report, he had a heated discussion with teachers and students in BEBC.