Incremental classification learning for anomaly detection in medical images

Balathasan Giritharan; Xiaohui Yuan; Jianguo Liu

doi:10.1117/12.812463

27 February 2009 Incremental classification learning for anomaly detection in medical images

Balathasan Giritharan, Xiaohui Yuan, Jianguo Liu

Proceedings Volume 7260, Medical Imaging 2009: Computer-Aided Diagnosis; 72603W (2009) https://doi.org/10.1117/12.812463
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Computer-aided diagnosis usually screens thousands of instances to find only a few positive cases that indicate probable presence of disease.The amount of patient data increases consistently all the time. In diagnosis of new instances, disagreement occurs between a CAD system and physicians, which suggests inaccurate classifiers. Intuitively, misclassified instances and the previously acquired data should be used to retrain the classifier. This, however, is very time consuming and, in some cases where dataset is too large, becomes infeasible. In addition, among the patient data, only a small percentile shows positive sign, which is known as imbalanced data.We present an incremental Support Vector Machines(SVM) as a solution for the class imbalance problem in classification of anomaly in medical images. The support vectors provide a concise representation of the distribution of the training data. Here we use bootstrapping to identify potential candidate support vectors for future iterations. Experiments were conducted using images from endoscopy videos, and the sensitivity and specificity were close to that of SVM trained using all samples available at a given incremental step with significantly improved efficiency in training the classifier.

Citation Download Citation

Balathasan Giritharan, Xiaohui Yuan, and Jianguo Liu "Incremental classification learning for anomaly detection in medical images", Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72603W (27 February 2009); https://doi.org/10.1117/12.812463

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