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Impact of deep learning-based image super-resolution on binary signal detection.

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Learning efficient channels with a dual loss autoencoder., , и . Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, том 11316 из SPIE Proceedings, стр. 113160C. SPIE, (2020)Autoencoder embedding of task-specific information., , и . Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, том 10952 из SPIE Proceedings, стр. 1095207. SPIE, (2019)SlabGAN: a method for generating efficient 3D anisotropic medical volumes using generative adversarial networks., , , и . Medical Imaging: Image Processing, том 11596 из SPIE Proceedings, SPIE, (2021)Learned Hotelling observers for use with multi-modal data., , , и . Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, том 12035 из SPIE Proceedings, SPIE, (2022)Task-based evaluation of deep image super-resolution in medical imaging., , , , и . Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, том 11599 из SPIE Proceedings, SPIE, (2021)Impact of deep learning-based image super-resolution on binary signal detection., , , , и . CoRR, (2021)Approximating the Hotelling Observer with Autoencoder-Learned Efficient Channels for Binary Signal Detection Tasks., , и . CoRR, (2020)Advancing the AmbientGAN for learning stochastic object models., , , , , и . Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, том 11599 из SPIE Proceedings, SPIE, (2021)