Liyang Wei

TL;DR: This paper investigated several state-of-the-art machine-learning methods for automated classification of clustered microcalcifications (MCs), and formulated differentiation of malignant from benign MCs as a supervised learning problem, and applied these learning methods to develop the classification algorithm.

TL;DR: This paper forms MC detection as a supervised-learning problem, and applies RVM as a classifier to determine at each location in the mammogram if an MC object is present or not, and develops computerized detection algorithms that are not only accurate but also computationally efficient for MC detection in mammograms.

TL;DR: The experimental results on a mammogram database demonstrate that the proposed retrieval-driven approach with an adaptive support vector machine (SVM) could improve the classification performance from 0.78 to 0.82 in terms of the area under the ROC curve.

TL;DR: A channelized support vector machine (CSVM) is developed which is compared to the channelized Hotelling observer (CHO) in terms of its ability to predict human-observer performance and finds that the CSVM is better able to generalize to unseen images than the CHO, and therefore may represent a useful improvement on the CHO methodology, while retaining its essential features.

TL;DR: The experimental results on a mammogram database demonstrate that the proposed retrieval-driven approach with an adaptive support vector machine (SVM) could improve the classification performance from 0.78 to 0.82 in terms of the area under the ROC curve.