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

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發(fā)表于 2025-3-21 16:51:52 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Deep Generative Models
副標(biāo)題Second MICCAI Worksh
編輯Anirban Mukhopadhyay,Ilkay Oksuz,Yixuan Yuan
視頻videohttp://file.papertrans.cn/265/264555/264555.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Deep Generative Models; Second MICCAI Worksh Anirban Mukhopadhyay,Ilkay Oksuz,Yixuan Yuan Conference proceedings 2022 The Editor(s) (if app
描述This book constitutes the refereed proceedings of the Second MICCAI Workshop on Deep Generative Models, DG4MICCAI 2022, held in conjunction with MICCAI 2022, in September 2022. The workshops took place in Singapore.?.DG4MICCAI 2022 accepted 12 papers from the 15 submissions received. The workshop focusses on recent algorithmic developments, new results, and?promising future directions in Deep Generative Models. Deep generative models?such 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..
出版日期Conference proceedings 2022
關(guān)鍵詞artificial intelligence; bioinformatics; color image processing; color images; computer vision; digital i
版次1
doihttps://doi.org/10.1007/978-3-031-18576-2
isbn_softcover978-3-031-18575-5
isbn_ebook978-3-031-18576-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Deep Generative Models978-3-031-18576-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
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Abstract Factory (Abstract Factory),s clinical applications. Deep learning-based super-resolution methods provided promising results for improving the spatial resolution of MRSI, but the super-resolved images are often blurry compared to the experimentally-acquired high-resolution images. Attempts have been made with the generative ad
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Wilian Gatti Jr,Beaumie Kim,Lynde Tanage analysis. The latent spaces of these models often show semantically meaningful directions corresponding to human-interpretable image transformations. However, until now, their exploration for medical images has been limited due to the requirement of supervised data. Several methods for unsupervi
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發(fā)表于 2025-3-22 21:15:59 | 只看該作者
Wilian Gatti Jr,Beaumie Kim,Lynde Tane problem of inferring pixel-level predictions of brain lesions by only using image-level labels for training. By leveraging recent advances in generative diffusion probabilistic models (DPM), we synthesize counterfactuals of “How would a patient appear if . pathology was not present?”. The differen
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Requirements and Specificationslete, or have inconsistencies between observations. Thus, we propose a generative model that not only produces continuous trajectories of fully synthetic patient images, but also imputes missing data in existing trajectories, by estimating realistic progression over time. Our generative model is tra
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