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Yongyi Yang
Researcher at Illinois Institute of Technology
Publications - 275
Citations - 7253
Yongyi Yang is an academic researcher from Illinois Institute of Technology. The author has contributed to research in topics: Iterative reconstruction & Gated SPECT. The author has an hindex of 36, co-authored 260 publications receiving 6817 citations. Previous affiliations of Yongyi Yang include University of Toronto & Beijing Jiaotong University.
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A support vector machine approach for detection of microcalcifications
TL;DR: The ability of SVM to outperform several well-known methods developed for the widely studied problem of MC detection suggests that SVM is a promising technique for object detection in a medical imaging application.
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Computer-Aided Detection and Diagnosis of Breast Cancer With Mammography: Recent Advances
TL;DR: An overview of recent advances in the development of CAD systems and related techniques for breast cancer detection and diagnosis focuses on key CAD techniques developed recently, including detection of calcifications, detection of masses, Detection of architectural distortion, detectionof bilateral asymmetry, image enhancement, and image retrieval.
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Regularized reconstruction to reduce blocking artifacts of block discrete cosine transform compressed images
TL;DR: The reconstruction of images from incomplete block discrete cosine transform (BDCT) data is examined and two methods are proposed for solving this regularized recovery problem based on the theory of projections onto convex sets (POCS) and the constrained least squares (CLS).
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Projection-based spatially adaptive reconstruction of block-transform compressed images
TL;DR: A spatially adaptive image recovery algorithm is proposed based on the theory of projections onto convex sets that captures both the local statistical properties of the image and the human perceptual characteristics.
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A study on several Machine-learning methods for classification of Malignant and benign clustered microcalcifications
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.