@inproceedings{242dd771ec64428884332dccdc61f8e6,
title = "A three-dimensional detector based on focal loss for pulmonary nodules detection",
abstract = "The problem of class imbalance exists in detecting the pulmonary nodules from Computed Tomography (CT) by means of convolutional neural network. A Three-Dimensional Detector Based on Focal Loss (FLTDD) is designed in this paper to ensure that the pulmonary nodules in CT could be identified more exactly. Its framework focuses more on samples that are difficult to be classified. Besides, three dimensional detector contains richer spatial information and gets more distinguishing features. The experiment results obtained from LIDC-IDRI data set show that the average sensitivity score of FLTDD achieves 89.62%. It has a 1.47% improvement compared with the published CASED method.",
keywords = "3D Convolutional Neural Network, Class Imbalance, Focal Loss, Pulmonary Nodule Detection",
author = "Lei Wang and Yaping Dai and Zhiyang Jia and Yongkang Nie and Liang Liu",
note = "Publisher Copyright: {\textcopyright} 2019 Technical Committee on Control Theory, Chinese Association of Automation.; 38th Chinese Control Conference, CCC 2019 ; Conference date: 27-07-2019 Through 30-07-2019",
year = "2019",
month = jul,
doi = "10.23919/ChiCC.2019.8866509",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "8445--8449",
editor = "Minyue Fu and Jian Sun",
booktitle = "Proceedings of the 38th Chinese Control Conference, CCC 2019",
address = "United States",
}