Titlebook: Deep Generative Models; Second MICCAI Worksh Anirban Mukhopadhyay,Ilkay Oksuz,Yixuan Yuan Conference proceedings 2022 The Editor(s) (if app

euphoria

COLIC

3D (c)GAN for Whole Body MR Synthesises 3D medical images. The model can easily be conditioned on meta data, for example available patient information. We evaluate the quality of the generated images and compare our model against the 3D-StyleGAN model which is also designed for 3D medical image synthesis.

Mercantile

Conference proceedings 2022rative Adversarial Network (GAN) and Variational Auto-Encoder (VAE) are currently receiving widespread attention from not only the computer vision and machine learning communities, but also in the MIC and CAI community..

高谈阔论

0302-9743 ch as Generative Adversarial Network (GAN) and Variational Auto-Encoder (VAE) are currently receiving widespread attention from not only the computer vision and machine learning communities, but also in the MIC and CAI community..978-3-031-18575-5978-3-031-18576-2Series ISSN 0302-9743 Series E-ISSN 1611-3349

deadlock

萤火虫

Abstract Factory (Abstract Factory),the transformer via cross-attention, i.e. supplying anatomical reference information from paired CT images to aid the PET anomaly detection task. Using 83 whole-body PET/CT samples containing various cancer types, we show that our anomaly detection method is robust and capable of achieving accurate

CHYME

ALT

The Abuse of Discretionary PowerIPF. ATN was shown to be quicker and easier to train than simGAN. ATN-based airway measurements showed consistently stronger associations with mortality than simGAN-derived airway metrics on IPF CTs. Airway synthesis by a transformation network that refines synthetic data using perceptual losses is

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