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Impact of loss functions on the performance of a deep neural network designed to restore low-dose digital mammography., , , , и . Artif. Intell. Medicine, (августа 2023)Convolutional Neural Network to Restore Low-Dose Digital Breast Tomosynthesis Projections in a Variance Stabilization Domain., , , , , и . CoRR, (2022)Data Augmentation for Detection of Architectural Distortion in Digital Mammography using Deep Learning Approach., , , , , и . CoRR, (2018)A cross-cutting approach for tracking architectural distortion locii on digital breast tomosynthesis slices., , , , , , , и . Biomed. Signal Process. Control., (2019)Poisson Wiener filtering with non-local weighted parameter estimation using stochastic distances., , и . Signal Process., (2018)Method for Simulating Dose Reduction in Digital Breast Tomosynthesis., , , , , и . IEEE Trans. Medical Imaging, 36 (11): 2331-2342 (2017)A Novel Fusion-Based Texture Descriptor to Improve the Detection of Architectural Distortion in Digital Mammography., , , , , и . J. Digit. Imaging, 34 (1): 36-52 (2021)Impact of loss functions on the performance of a deep neural network designed to restore low-dose digital mammography., , , , и . CoRR, (2021)Exploratory learning with convolutional autoencoder for discrimination of architectural distortion in digital mammography., , , , и . Medical Imaging: Computer-Aided Diagnosis, том 10950 из SPIE Proceedings, стр. 109502F. SPIE, (2019)Unbiased Injection of Signal-Dependent Noise in Variance-Stabilized Range., , и . IEEE Signal Process. Lett., 23 (10): 1494-1498 (2016)