Recently, lung nodule detection methods based on deep learning have shown excellent performance in the medical image processing field. Considering that only a few public lung datasets are available and lung nodules are more difficult to detect in CT images than in natural images, the existing methods face many bottlenecks when detecting lung nodules, especially hard ones in CT images. In order to solve these problems, we plan to enhance the focus of our network. In this work, we present an improved detection network that pays more attention to hard samples and datasets to deal with lung nodules by introducing deformable convolution and self-paced learning. Experiments on the LUNA16 dataset demonstrate the effectiveness of our proposed components and show that our method has reached competitive performance.

翻译：近年来，基于深度学习的肺结节检测方法在医学图像处理领域展现出卓越性能。考虑到可用的公共肺部数据集有限，且CT图像中的肺结节比自然图像更难检测，现有方法在检测肺结节（尤其是CT图像中的困难样本）时面临诸多瓶颈。为解决这些问题，我们计划增强网络对困难样本的关注度。本研究提出一种改进的检测网络，通过引入可变形卷积和自步学习机制，更注重处理肺结节检测中的困难样本与数据集。在LUNA16数据集上的实验验证了所提出组件的有效性，表明该方法已达到具有竞争力的性能水平。