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Eliminating false-positive microcalcification clusters in a mammography CAD scheme using a Bayesian neural network., , , , и . Medical Imaging: Image Processing, том 4322 из SPIE Proceedings, SPIE, (2001)Reconstruction-Aware Imaging System Ranking by Use of a Sparsity-Driven Numerical Observer Enabled by Variational Bayesian Inference., , , , и . IEEE Trans. Med. Imaging, 38 (5): 1251-1262 (2019)Observer models utilizing compressed textures., и . Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, том 11599 из SPIE Proceedings, SPIE, (2021)Multiobjective Genetic Optimization of Diagnostic Classifiers with Implications for Generating ROC Curves., и . IEEE Trans. Med. Imaging, 18 (8): 675-685 (1999)Multiobjective genetic optimization of diagnostic classifiers used in the computerized detection of mass lesions in mammography., , и . Medical Imaging: Image Processing, том 3979 из SPIE Proceedings, SPIE, (2000)Automated Seeded Lesion Segmentation on Digital Mammograms., и . IEEE Trans. Med. Imaging, 17 (4): 510-517 (1998)Image-Quality Assessment in Optical Tomography., и . ISBI, стр. 1471-1474. IEEE, (2004)Adaptive small-animal SPECT/CT., , , , , , , , и . ISBI, стр. 1407-1410. IEEE, (2008)Objective Comparison of Quantitative Imaging Modalities Without the Use of a Gold Standard., , , , и . IPMI, том 2082 из Lecture Notes in Computer Science, стр. 12-23. Springer, (2001)Development of an Ideal Observer that Incorporates Nuisance Parameters and Processes List-Mode Data., , , , и . CoRR, (2016)