Multiple-instance learning for computer-aided detection of tuberculosis

J. Melendez; C. I. Sánchez; R. H. H. M. Philipsen; P. Maduskar; B. van Ginneken

doi:10.1117/12.2043018

20 March 2014 Multiple-instance learning for computer-aided detection of tuberculosis

J. Melendez, C. I. Sánchez, R. H. H. M. Philipsen, P. Maduskar, B. van Ginneken

Proceedings Volume 9035, Medical Imaging 2014: Computer-Aided Diagnosis; 90351J (2014) https://doi.org/10.1117/12.2043018
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

Detection of tuberculosis (TB) on chest radiographs (CXRs) is a hard problem. Therefore, to help radiologists or even take their place when they are not available, computer-aided detection (CAD) systems are being developed. In order to reach a performance comparable to that of human experts, the pattern recognition algorithms of these systems are typically trained on large CXR databases that have been manually annotated to indicate the abnormal lung regions. However, manually outlining those regions constitutes a time-consuming process that, besides, is prone to inconsistencies and errors introduced by interobserver variability and the absence of an external reference standard. In this paper, we investigate an alternative pattern classi cation method, namely multiple-instance learning (MIL), that does not require such detailed information for a CAD system to be trained. We have applied this alternative approach to a CAD system aimed at detecting textural lesions associated with TB. Only the case (or image) condition (normal or abnormal) was provided in the training stage. We compared the resulting performance with those achieved by several variations of a conventional system trained with detailed annotations. A database of 917 CXRs was constructed for experimentation. It was divided into two roughly equal parts that were used as training and test sets. The area under the receiver operating characteristic curve was utilized as a performance measure. Our experiments show that, by applying the investigated MIL approach, comparable results as with the aforementioned conventional systems are obtained in most cases, without requiring condition information at the lesion level.

Citation Download Citation

J. Melendez, C. I. Sánchez, R. H. H. M. Philipsen, P. Maduskar, and B. van Ginneken "Multiple-instance learning for computer-aided detection of tuberculosis", Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90351J (20 March 2014); https://doi.org/10.1117/12.2043018

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available