Improved mask R-CNN for lung nodule segmentation

Huanlan Yan, Huijuan Lu, Minchao Ye, Ke Yan, Yige Xu, Qun Jin

研究成果: Conference contribution

抄録

With more and more people suffer from lung cancer, computer-aided diagnosis plays a more and more important role in lung cancer diagnosis. CNN has achieved state-of-the-art performance in image processing, and Mask R-CNN outperforms most other methods on instance segmentation. However, the target is extraordinarily small, and the background is very large in images, which results in a large number of negative examples and most of them are easy negatives. They will contribute a large part of the loss value in smooth loss function. The class imbalance problem leads to inefficient training, which makes model degenerated. In this paper, we propose a method based on Mask R-CNN to segment lung nodules. Due to the non-uniformity of CT values, we use the Laplacian operator to do feature dimensionality reduction for filtering out part of the noise. In our model, the novel function Focal Loss is used to suppress well-classified examples. The model is tested on LIDC-IDRI dataset and the results showed that the average precision of lung nodules reaches 78%. Compared with the smooth loss function in Mask R-CNN it improves by 7%.

本文言語English
ホスト出版物のタイトルProceedings - 10th International Conference on Information Technology in Medicine and Education, ITME 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ページ137-141
ページ数5
ISBN(電子版)9781728139173
DOI
出版ステータスPublished - 2019 8
イベント10th International Conference on Information Technology in Medicine and Education, ITME 2019 - Qingdao, Shandong, China
継続期間: 2019 8 232019 8 25

出版物シリーズ

名前Proceedings - 10th International Conference on Information Technology in Medicine and Education, ITME 2019

Conference

Conference10th International Conference on Information Technology in Medicine and Education, ITME 2019
CountryChina
CityQingdao, Shandong
Period19/8/2319/8/25

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management
  • Health Informatics
  • Education

フィンガープリント 「Improved mask R-CNN for lung nodule segmentation」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル