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Lung nodules suffer large variation in size and appearance in CT images.Nodules less than 10mm can easily lose information after down-sampling inconvolutional neural networks, which results in low sensitivity. In this paper,a combination of 3D image and feature pyramid is exploited to integratelower-level texture features with high-level semantic features, thus leading toa higher recall. However, 3D operations are time and memory consuming, whichaggravates the situation with the explosive growth of medical images. To tacklethis problem, we propose a general curriculum training strategy to speed uptraining. An dynamic sampling method is designed to pick up partial sampleswhich give the best contribution to network training, thus leading to much lesstime consuming. In experiments, we demonstrate that the proposed networkoutperforms previous state-of-the-art methods. Meanwhile, our sampling strategyhalves the training time of the proposal network on LUNA16.

Integrating Feature and Image Pyramid: A Lung Nodule Detector Learned in Curriculum Fashion