Computer-Aided Diagnosis in Chest Radiography with Deep Multi-Instance Learning

  • Kang Qu
  • Xiangfei Chai
  • Tianjiao Liu
  • Yadong Zhang
  • Biao Leng
  • Zhang Xiong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10637)

Abstract

The Computer-Aided Diagnosis (CAD) for chest X-ray image has been investigated for many years. However, it has not been widely used since limited accuracy. Deep learning opens a new era for image recognition and classification. We propose a novel framework called Deep Multi-Instance Learning (DMIL) on chest radiographic images diagnosis, which combines deep learning and multi-instance learning. Besides, we preprocess images with the alignment based on the key points. This framework can effectively improve the diagnosis effect in the image level annotation. We quantify the framework on three datasets, respectively with different amounts and different classification tasks. The proposed framework obtained the AUC of 0.986, 0.873, 0.824 respectively in classification tasks of the enlarged heart, the pulmonary nodule, and the abnormal. The experiments we implement demonstrate that the proposed framework outperforms the other methods in various evaluation criteria.

Keywords

Chest radiograph Deep learning Multi-Instance Learning Medical image 

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. 61472023) and the State Key Laboratory of Software Development Environment (No. SKLSDE-2016ZX-24).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Kang Qu
    • 1
  • Xiangfei Chai
    • 2
  • Tianjiao Liu
    • 3
  • Yadong Zhang
    • 2
  • Biao Leng
    • 4
  • Zhang Xiong
    • 4
  1. 1.School of Computer Science and EngineeringBeihang UniversityBeijingChina
  2. 2.Huiying Medical Technology Inc. (Beijing)BeijingChina
  3. 3.Department of Electronic EngineeringTsinghua UniversityBeijingChina
  4. 4.State Key Laboratory of Software Development Environment, School of Computer Science and EngineeringBeihang UniversityBeijingChina

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