Titlebook: Deep Generative Models; Third MICCAI Worksho Anirban Mukhopadhyay,Ilkay Oksuz,Yixuan Yuan Conference proceedings 2024 The Editor(s) (if app

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书目名称Deep Generative Models影响因子(影响力)




书目名称Deep Generative Models影响因子(影响力)学科排名




书目名称Deep Generative Models网络公开度




书目名称Deep Generative Models网络公开度学科排名




书目名称Deep Generative Models被引频次




书目名称Deep Generative Models被引频次学科排名




书目名称Deep Generative Models年度引用




书目名称Deep Generative Models年度引用学科排名




书目名称Deep Generative Models读者反馈




书目名称Deep Generative Models读者反馈学科排名

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ViT-DAE: Transformer-Driven Diffusion Autoencoder for Histopathology Image AnalysisViT) and diffusion autoencoders for high-quality histopathology image synthesis. This marks the first time that ViT has been introduced to diffusion autoencoders in computational pathology, allowing the model to better capture the complex and intricate details of histopathology images. We demonstrat

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Importance of Aligning Training Strategy with Evaluation for Diffusion Models in 3D Multiclass Segmen-test discrepancy, including performing mask prediction, using Dice loss, and reducing the number of diffusion time steps during training. The performance of diffusion models was also competitive and visually similar to non-diffusion-based U-net, within the same compute budget. The JAX-based diffus

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反叛者

头脑冷静

gain631

Shape-Guided Conditional Latent Diffusion Models for Synthesising Brain Vasculaturebserved that our model generated CoW variants that are more realistic and demonstrate higher visual fidelity than competing approaches with an FID score 53% better than the best-performing GAN-based model.

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