Feature selection and classifiers for the computerized detection of mass lesions in digital mammography. - Matthew A. Kupinski; Maryellen L. Giger (ICNN, 1997)

Optimization and FROC Analysis of Rule-Based Detection Schemes Using a Multiobjective Approach. - Mark A. Anastasio; Matthew A. Kupinski; Robert M. Nishikawa (IEEE Trans. Medical Imaging, 1998)

Automated Seeded Lesion Segmentation on Digital Mammograms. - Matthew A. Kupinski; Maryellen L. Giger (IEEE Trans. Medical Imaging, 1998)

Multiobjective Genetic Optimization of Diagnostic Classifiers with Implications for Generating ROC Curves. - Matthew A. Kupinski; Mark A. Anastasio (IEEE Trans. Medical Imaging, 1999)

Multiobjective genetic optimization of diagnostic classifiers used in the computerized detection of mass lesions in mammography. - Matthew A. Kupinski; Mark A. Anastasio; Maryellen Lissak Giger (Image Processing, 2000)

Eliminating false-positive microcalcification clusters in a mammography CAD scheme using a Bayesian neural network. - Darrin C. Edwards; John Papaioannou; Yulei Jiang; Matthew A. Kupinski; Robert M. Nishikawa (Image Processing, 2001)

Ideal Observer Approximation Using Bayesian Classification Neural Networks. - Matthew A. Kupinski; Darrin C. Edwards; Maryellen L. Giger; Charles E. Metz (IEEE Trans. Medical Imaging, 2001)

Objective Comparison of Quantitative Imaging Modalities Without the Use of a Gold Standard. - John W. Hoppin; Matthew A. Kupinski; George A. Kastis; Eric Clarkson; Harrison H. Barrett (IEEE Trans. Medical Imaging, 2002)

Ideal-Observer Performance under Signal and Background Uncertainty. - Subok Park; Matthew A. Kupinski; Eric Clarkson; Harrison H. Barrett (IPMI, 2003)

Guest editor's introduction - Computing in optics. - Matthew A. Kupinski (Comput. Sci. Eng., 2003)

Image-Quality Assessment in Optical Tomography. - Matthew A. Kupinski; Eric Clarkson (ISBI, 2004)

Ideal observers and optimal ROC hypersurfaces in N-class classification. - Darrin C. Edwards; Charles E. Metz; Matthew A. Kupinski (IEEE Trans. Medical Imaging, 2004)

Adaptive small-animal SPECT/CT. - L. R. Furenlid; J. W. Moore; M. Freed; Matthew A. Kupinski; Eric Clarkson; Z. Liu; D. W. Wilson; James M. Woolfenden; Harrison H. Barrett (ISBI, 2008)

A Task-Based Approach to Adaptive and Multimodality Imaging. - Eric Clarkson; Matthew A. Kupinski; Harrison H. Barrett; Lars R. Furenlid (Proc. IEEE, 2008)

Adaptive SPECT. - Harrison H. Barrett; L. R. Furenlid; M. Freed; J. Y. Hesterman; Matthew A. Kupinski; Eric Clarkson; M. K. Whitaker (IEEE Trans. Medical Imaging, 2008)

Global Compartmental Pharmacokinetic Models for Spatiotemporal SPECT and PET Imaging. - Eric Clarkson; Matthew A. Kupinski (SIAM J. Imaging Sci., 2009)

Quantitative image quality evaluation for cardiac CT reconstructions. - Hsin-Wu Tseng; Jiahua Fan; Matthew A. Kupinski; William Balhorn; Darin R. Okerlund (Image Perception, Observer Performance, and Technology Assessment, 2016)

Development of an Ideal Observer that Incorporates Nuisance Parameters and Processes List-Mode Data. - Christopher J. MacGahan; Matthew A. Kupinski; Nathan R. Hilton; Erik M. Brubaker; William C. Johnson (CoRR, 2016)

Reconstruction-Aware Imaging System Ranking by Use of a Sparsity-Driven Numerical Observer Enabled by Variational Bayesian Inference. - Yujia Chen 003; Yang Lou; Kun Wang 020; Matthew A. Kupinski; Mark A. Anastasio (IEEE Trans. Medical Imaging, 2019)

Observer-driven texture analysis in CT imaging. - Matthew A. Kupinski; Zachary Garrett; Jiahua Fan (Image Perception, Observer Performance, and Technology Assessment, 2020)

Observer models utilizing compressed textures. - Matthew A. Kupinski; Jiahua Fan (Image Perception, Observer Performance, and Technology Assessment, 2021)