Automatic Scoring of Multiple Semantic Attributes with Multi-Task Feature Leverage: A Study on Pulmonary Nodules in CT Images

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

Research output: Journal article publicationJournal articleAcademic researchpeer-review

60 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 this gap, we exploit three multi-task learning (MTL) schemes to leverage heterogeneous computational features derived from deep learning models of stacked denoising autoencoder (SDAE) and convolutional neural network (CNN), as well as hand-crafted Haar-like and HoG features, for the description of 9 semantic features for lung nodules in CT images. We regard that there may exist relations among the semantic features of 'spiculation', 'texture', 'margin', etc., that can be explored with the MTL. The Lung Image Database Consortium (LIDC) data is adopted in this study for the rich annotation resources. The LIDC nodules were quantitatively scored w.r.t. 9 semantic features from 12 radiologists of several institutes in U.S.A. By treating each semantic feature as an individual task, the MTL schemes select and map the heterogeneous computational features toward the radiologists' ratings with cross validation evaluation schemes on the randomly selected 2400 nodules from the LIDC dataset. The experimental results suggest that the predicted semantic scores from the three MTL schemes are closer to the radiologists' ratings than the scores from single-task LASSO and elastic net regression methods. The proposed semantic attribute scoring scheme may provide richer quantitative assessments of nodules for better support of diagnostic decision and management. Meanwhile, the capability of the automatic association of medical image contents with the clinical semantic terms by our method may also assist the development of medical search engine.
Original languageEnglish
Article number7745891
Pages (from-to)802-814
Number of pages13
JournalIEEE Transactions on Medical Imaging
Volume36
Issue number3
DOIs
Publication statusPublished - 1 Mar 2017

Keywords

  • Computed tomography (CT)
  • Computer-aided diagnosis (CAD)
  • Deep Learning
  • Feature learning
  • Lung nodule
  • Multi-task learning

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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