AUTHOR=Li Ping , Kong Xiangwen , Li Johann , Zhu Guangming , Lu Xiaoyuan , Shen Peiyi , Shah Syed Afaq Ali , Bennamoun Mohammed , Hua Tao TITLE=A Dataset of Pulmonary Lesions With Multiple-Level Attributes and Fine Contours JOURNAL=Frontiers in Digital Health VOLUME=2 YEAR=2021 URL=https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2020.609349 DOI=10.3389/fdgth.2020.609349 ISSN=2673-253X ABSTRACT=

Lung cancer is a life-threatening disease and its diagnosis is of great significance. Data scarcity and unavailability of datasets is a major bottleneck in lung cancer research. In this paper, we introduce a dataset of pulmonary lesions for designing the computer-aided diagnosis (CAD) systems. The dataset has fine contour annotations and nine attribute annotations. We define the structure of the dataset in detail, and then discuss the relationship of the attributes and pathology, and the correlation between the nine attributes with the chi-square test. To demonstrate the contribution of our dataset to computer-aided system design, we define four tasks that can be developed using our dataset. Then, we use our dataset to model multi-attribute classification tasks. We discuss the performance in 2D, 2.5D, and 3D input modes of the classification model. To improve performance, we introduce two attention mechanisms and verify the principles of the attention mechanisms through visualization. Experimental results show the relationship between different models and different levels of attributes.