A Novel Multiple-Instance Learning-Based Approach to Computer-Aided Detection of Tuberculosis on Chest X-Rays
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A Novel Multiple-Instance Learning-Based Approach to Computer-Aided Detection of Tuberculosis on Chest X-Rays
Abstract?To reach performance levels comparable to human experts, computeraided detection < CAD > systems are typically optimized following a supervised learning approach that relies onlarge training databases comprising manually annotated lesions. However, manuall y outlining those lesions constitutes a dif?cult and time-consuming process that renders detailedly annotated data dif?cult to obtain. We investigate an alternative approach, namely multiple-instance learning (MIL), that does not require detailed information for optimization. We have applied MIL to a CAD system for tuberculosis detection. Only the case condition (normal or abnormal) was required during training. Based upon the well-known miSVM technique, we propose an improved algorithm that overcomes miSVM?s drawbacks related to positive instance underestimation and costly iteration.
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| Domains | Bio Medical |
|---|---|
| Programming Language | Matlab |
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