Lung Nodule Detection with a 3D ConvNet via IoU Self-normalization and Maxout Unit
Fei Li, Hongyu Huang, Yawen Wu, Congbo Cai, Yue Huang, Xinghao Ding
Abstract
The automatic pulmonary nodule detection in thoracic computed tomography (CT) scans plays a crucial role in the early diagnosis of lung cancer. In this paper, we propose a novel framework with a 3D convolutional network (ConvNet) for pulmonary nodule detection. To improve the efficiency and flexibility, we adopt one-stage process without the false positive reduction stage. Specially, the great challenge of the nodule detection is the recall rate of small nodules. We propose two methods to solve this issue. Firstly, we set the classification label by the intersection over union (IoU) self-normalization, which enables to eliminate the loss of regression information caused by misleading classification confidence. Secondly, pulmonary nodules differ in size, shape and density, leading to large intra-class variations. We introduce maxout unit to solve this problem. Overall, we achieve an average FROC score of 0.912 on LUNA16 dataset, outperforming all other one-stage models as far as we know.
BibTeX
@inproceedings{icassp2019_lungnoduledetect,
title = {Lung Nodule Detection with a 3D ConvNet via IoU Self-normalization and Maxout Unit},
author = {Fei Li and Hongyu Huang and Yawen Wu and Congbo Cai and Yue Huang and Xinghao Ding},
booktitle = {ICASSP 2019},
year = {2019}
}