Improved Focus on Hard Samples for Lung Nodule Detection
This work addresses lung nodule detection for medical imaging, but it is incremental as it builds on existing deep learning methods with specific enhancements.
The paper tackled the problem of detecting hard lung nodules in CT images by introducing deformable convolution and self-paced learning, achieving competitive performance on the LUNA16 dataset.
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.