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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書(shū)目名稱Deep Generative Models影響因子(影響力)




書(shū)目名稱Deep Generative Models影響因子(影響力)學(xué)科排名




書(shū)目名稱Deep Generative Models網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Deep Generative Models網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Deep Generative Models被引頻次




書(shū)目名稱Deep Generative Models被引頻次學(xué)科排名




書(shū)目名稱Deep Generative Models年度引用




書(shū)目名稱Deep Generative Models年度引用學(xué)科排名




書(shū)目名稱Deep Generative Models讀者反饋




書(shū)目名稱Deep Generative Models讀者反饋學(xué)科排名

Galactogogue

,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

經(jīng)典

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

徹底明白

,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

徹底明白

,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

dura-mater

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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