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

Abstract

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

arXiv commentNodule Detection, Deep Learning, Curriculum Learning

References in corpus (4)

• Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge
• Focal Loss for Dense Object Detection
• Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks
• Automated Curriculum Learning for Neural Networks