Improved Focus on Hard Samples for Lung Nodule Detection
Yujiang Chen Mei Xie
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.