Automatic lung nodule detection in thoracic CT scans using dilated slice‐wise convolutions.

Purpose: Most state‐of‐the‐art automated medical image analysis methods for volumetric data rely on adaptations of two‐dimensional (2D) and three‐dimensional (3D) convolutional neural networks (CNNs). In this paper, we develop a novel unified CNN‐based model that combines the benefits of 2D and 3D networks for analyzing volumetric medical images. Methods: In our proposed framework, multiscale contextual information is first extracted from 2D slices inside a volume of interest (VOI). This is followed by dilated 1D convolutions across slices to aggregate in‐plane features in a slice‐wise manner and encode the information in the entire volume. Moreover, we formalize a curriculum learning strategy for a two‐stage system (i.e., a system that consists of screening and false positive reduction), where the training samples are presented to the network in a meaningful order to further improve the performance. Results: We evaluated the proposed approach by developing a computer‐aided detection (CADe) system for lung nodules. Our results on 888 CT exams demonstrate that the proposed approach can effectively analyze volumetric data by achieving a sensitivity of > 0.99 in the screening stage and a sensitivity of > 0.96 at eight false positives per case in the false positive reduction stage. Conclusion: Our experimental results show that the proposed method provides competitive results compared to state‐of‐the‐art 3D frameworks. In addition, we illustrate the benefits of curriculum learning strategies in two‐stage systems that are of common use in medical imaging applications. [ABSTRACT FROM AUTHOR]