Bridging computational features toward multiple semantic features with multi-task regression: A study of CT pulmonary nodules

Sihong Chen, Dong Ni, Jing Qin, Baiying Lei, Tianfu Wang, Jie Zhi Cheng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

18 Citations (Scopus)

Abstract

The gap between the computational and semantic features is the one of major factors that bottlenecks the computer-aided diagnosis (CAD) performance from clinical usage. To bridge such gap,we propose to utilize the multi-task regression (MTR) scheme that leverages heterogeneous computational features derived from deep learning models of stacked denoising autoencoder (SDAE) and convolutional neural network (CNN) as well as Haar-like features to approach 8 semantic features of lung CT nodules. We regard that there may exist relations among the semantic features of “spiculation”,“texture”,“margin”,etc.,that can be exploited with the multi-task learning technique. The Lung Imaging Database Consortium (LIDC) data is adopted for the rich annotations,where nodules were quantitatively rated for the semantic features from many radiologists. By treating each semantic feature as a task,the MTR selects and regresses the heterogeneous computational features toward the radiologists’ ratings with 10 fold cross-validation evaluation on the randomly selected LIDC 1400 nodules. The experimental results suggest that the predicted semantic scores from MTR are closer to the radiologists’ rating than the predicted scores from single-task LASSO and elastic net regression methods. The proposed semantic scoring scheme may provide richer quantitative assessments of nodules for deeper analysis and support more sophisticated clinical content retrieval in medical databases.
Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
PublisherSpringer Verlag
Pages53-60
Number of pages8
ISBN (Print)9783319467221
DOIs
Publication statusPublished - 1 Jan 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9901 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • CT
  • Deep learning
  • Lung nodule
  • Multi-task regression

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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