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A manifold learning regularization approach to enhance 3D CT image-based lung nodule classification

  • Ying Ren
  • Min-Yu Tsai
  • Liyuan Chen
  • Jing Wang
  • Shulong Li
  • Yufei Liu
  • Xun JiaEmail author
  • Chenyang ShenEmail author
Original Article

Abstract

Purpose

Diagnosis of lung cancer requires radiologists to review every lung nodule in CT images. Such a process can be very time-consuming, and the accuracy is affected by many factors, such as experience of radiologists and available diagnosis time. To address this problem, we proposed to develop a deep learning-based system to automatically classify benign and malignant lung nodules.

Methods

The proposed method automatically determines benignity or malignancy given the 3D CT image patch of a lung nodule to assist diagnosis process. Motivated by the fact that real structure among data is often embedded on a low-dimensional manifold, we developed a novel manifold regularized classification deep neural network (MRC-DNN) to perform classification directly based on the manifold representation of lung nodule images. The concise manifold representation revealing important data structure is expected to benefit the classification, while the manifold regularization enforces strong, but natural constraints on network training, preventing over-fitting.

Results

The proposed method achieves accurate manifold learning with reconstruction error of ~ 30 HU on real lung nodule CT image data. In addition, the classification accuracy on testing data is 0.90 with sensitivity of 0.81 and specificity of 0.95, which outperforms state-of-the-art deep learning methods.

Conclusion

The proposed MRC-DNN facilitates an accurate manifold learning approach for lung nodule classification based on 3D CT images. More importantly, MRC-DNN suggests a new and effective idea of enforcing regularization for network training, possessing the potential impact to a board range of applications.

Keywords

Diagnosis Lung nodule classification Deep learning Regularization Manifold learning 

Notes

Acknowledgements

This work was supported by the China National Key Research and Development Program of China (Grant Nos. 2016YFE0125200 and 2016YFC0101100) and the China Fundamental Research Funds for the Central Universities (Grant No. 2019CDQYGD020).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Human and animal rights

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

This article does not contain patient data.

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Copyright information

© CARS 2019

Authors and Affiliations

  1. 1.Department of NeurologyHeilongjiang Province Number III HospitalBeianChina
  2. 2.Innovative Technology Of Radiotherapy Computations and Hardware (iTORCH) LaboratoryUniversity of Texas Southwestern Medical CenterDallasUSA
  3. 3.Medical Artificial Intelligence and Automation (MAIA) LaboratoryUniversity of Texas Southwestern Medical CenterDallasUSA
  4. 4.Department of Radiation OncologyUniversity of Texas Southwestern Medical CenterDallasUSA
  5. 5.Key Laboratory of Optoelectronic Technology and Systems, Ministry of EducationChongqing UniversityChongqingChina
  6. 6.Centre for Intelligent Sensing Technology, College of Optoelectronic EngineeringChongqing UniversityChongqingChina

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