Title:
SYSTEM FOR AUTOMATIC DIAGNOSIS AND PROGNOSIS OF TUBERCULOSIS BY CAD-BASED DIGITAL X-RAY
Document Type and Number:
WIPO Patent Application WO/2017/069596
Kind Code:
A1
Abstract:
The present invention relates to a system for automatic diagnosis and prognosis of tuberculosis by a CAD-based digital X-ray, and more particularly, to a system for automatically diagnosing and predicting whether a patient has been infected with tuberculosis, by applying a deep learning algorithm to big data associated with a tuberculosis X-ray image. The system for automatic diagnosis and prognosis of tuberculosis by the CAD-based digital X-ray according to the present invention supports a general-purpose approach through a digital X-ray and picture archiving & communication system (PACS) through an appropriate technology-based approach for export to developing countries, and thus, improves efficiency of diagnosis. Also, the system for automatic diagnosis and prognosis of tuberculosis by the CAD-based digital X-ray according to the present invention pushes forward supporting big data-based prognosis and customized diagnosis of tuberculosis on the basis of development and trends of tuberculosis patients through CAD-based pre-screening.
Inventors:
LEE MINHWA (KR)
KIM HYOEUN (KR)
HWANG SANGHEUM (KR)
PAEK SEUNGWOOK (KR)
LEE JUNGIN (KR)
JANG MINHONG (KR)
KIM HYOEUN (KR)
HWANG SANGHEUM (KR)
PAEK SEUNGWOOK (KR)
LEE JUNGIN (KR)
JANG MINHONG (KR)
Application Number:
PCT/KR2016/011973
Publication Date:
April 27, 2017
Filing Date:
October 24, 2016
Export Citation:
Assignee:
KOREA DIGITAL HOSPITAL EXP AGENCY (KR)
LUNIT INC (KR)
LUNIT INC (KR)
International Classes:
G06F19/00; A61B5/00; A61B5/08; A61B6/00; G06N99/00
Foreign References:
| US20140010433A1 | 2014-01-09 |
Other References:
TAN, JEN HONG ET AL.: "Computer-Assisted Diagnosis of Tuberculosis: A First Order Statistical Approach to Chest Radiograph", JOURNAL OF MEDICAL SYSTEMS, vol. 36, no. 5, 7 July 2011 (2011-07-07), pages 2751 - 2759, XP035103419, Retrieved from the Internet
BAR, YANIV ET AL.: "Deep Learning with Non-medical Training Used for Chest Pathology Identification", SPIE PROCEEDINGS VOL. 9414 , MEDICAL IMAGING 2015: COMPUTER-AIDED DIAGNOSIS, vol. 9414, 20 March 2015 (2015-03-20), pages 1 - 7, XP060052074, Retrieved from the Internet
ZHANG, BAILING ET AL.: "Spectral Regression Dimension Reduction for Multiple Features Facial Image Retrieval", INTERNATIONAL JOURNAL OF BIOMETRICS, vol. 4, 1 December 2012 (2012-12-01), pages 77 - 101, Retrieved from the Internet
NGO, TUAN ANH ET AL.: "Lung Segmentation in Chest Radiographs Using Distance Regularizedlevel Set and Deep-structured Learning and Inference", IMAGE PROCESSING (ICIP), 2015 IEEE INTERNATIONAL CONFERENCE, 27 September 2015 (2015-09-27), pages 2140 - 2143, XP032826801, Retrieved from the Internet
BAR, YANIV ET AL.: "Deep Learning with Non-medical Training Used for Chest Pathology Identification", SPIE PROCEEDINGS VOL. 9414 , MEDICAL IMAGING 2015: COMPUTER-AIDED DIAGNOSIS, vol. 9414, 20 March 2015 (2015-03-20), pages 1 - 7, XP060052074, Retrieved from the Internet
ZHANG, BAILING ET AL.: "Spectral Regression Dimension Reduction for Multiple Features Facial Image Retrieval", INTERNATIONAL JOURNAL OF BIOMETRICS, vol. 4, 1 December 2012 (2012-12-01), pages 77 - 101, Retrieved from the Internet
NGO, TUAN ANH ET AL.: "Lung Segmentation in Chest Radiographs Using Distance Regularizedlevel Set and Deep-structured Learning and Inference", IMAGE PROCESSING (ICIP), 2015 IEEE INTERNATIONAL CONFERENCE, 27 September 2015 (2015-09-27), pages 2140 - 2143, XP032826801, Retrieved from the Internet
Attorney, Agent or Firm:
PARK, Junghak (KR)
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