Titlebook: Deep Generative Models; 4th MICCAI Workshop, Anirban Mukhopadhyay,Ilkay Oksuz,Yixuan Yuan Conference proceedings 2025 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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,WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image Synthesis,mostly apply patch-wise, slice-wise, or cascaded generation techniques to fit the high-dimensional data into the limited GPU memory. However, these approaches may introduce artifacts and potentially restrict the model’s applicability for certain downstream tasks. This work presents WDM, a wavelet-ba

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,Anatomically-Guided Inpainting for Local Synthesis of Normal Chest Radiographs,ct to variability. As such, automated systems for pathology detection have been proposed and promising results have been obtained, particularly using deep learning. However, these tools suffer from poor explainability, which represents a major hurdle for their adoption in clinical practice. One prop

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Enhancing Cross-Modal Medical Image Segmentation Through Compositionality,rasts, and appearances of anatomical structures. We introduce compositionality as an inductive bias in a cross-modal segmentation network to improve segmentation performance and interpretability while reducing complexity. The proposed network is an end-to-end cross-modal segmentation framework that

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,Unpaired Modality Translation for Pseudo Labeling of Histology Images,ge. We propose a microscopy pseudo labeling pipeline utilizing unsupervised image translation to address this issue. Our method generates pseudo labels by translating between labeled and unlabeled domains without requiring prior annotation in the target domain. We evaluate two pseudo labeling strate

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,SNAFusion: Distilling 2D Axial Plane Diffusion Priors for Sparse-View 3D Cone-Beam CT Imaging, challenging inverse problem. Previous existing data-driven techniques employ 3D decoders with paired huge volumes of training datasets, resulting in limited generalizability and also ignoring the fact of clinical dataset shortage. Even though some implicit neural rendering (INR) methods are focused

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SynthBrainGrow: Synthetic Diffusion Brain Aging for Longitudinal MRI Data Generation in Young Peopl. Synthetically generated, age-adjusted brain images could serve as valuable alternatives to costly longitudinal imaging acquisitions, serve as internal controls for studies looking at the effects of environmental or therapeutic modifiers on brain development, and allow data augmentation for diverse

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,Denoising Diffusion Models for 3D Healthy Brain Tissue Inpainting, volumetric changes. However, many of the evaluation tools are optimized for analyzing healthy tissue. To enable the evaluation of scans containing pathological tissue, it is therefore required to restore healthy tissue in the pathological areas. In this work, we explore and extend denoising diffusi

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