Papers Medical Image Generation
“Medical Image Generation” 태그가 달린 논문 97편 · 필터 해제
Med-Art: Diffusion Transformer for 2D Medical Text-to-Image Generation
Text-to-image generative models have achieved remarkable breakthroughs in recent years. However, their application in medical image generation still faces significant challenges, including small dataset sizes, and scarci…
Image GenerationMedical Image GenerationText to Image GenerationText-to-Image GenerationMetrics that matter: Evaluating image quality metrics for medical image generation
Evaluating generative models for synthetic medical imaging is crucial yet challenging, especially given the high standards of fidelity, anatomical accuracy, and safety required for clinical applications. Standard evaluat…
Image GenerationMedical Image GenerationCausal Disentanglement for Robust Long-tail Medical Image Generation
Counterfactual medical image generation effectively addresses data scarcity and enhances the interpretability of medical images. However, due to the complex and diverse pathological features of medical images and the imb…
counterfactualDisentanglementImage GenerationLarge Language Model+1seg2med: a bridge from artificial anatomy to multimodal medical images
We present seg2med, a modular framework for anatomy-driven multimodal medical image synthesis. The system integrates three components to enable high-fidelity, cross-modality generation of CT and MR images based on struct…
AnatomyData AugmentationDenoisingDiagnostic+3Latent Diffusion Autoencoders: Toward Efficient and Meaningful Unsupervised Representation Learning in Medical Imaging
This study presents Latent Diffusion Autoencoder (LDAE), a novel encoder-decoder diffusion-based framework for efficient and meaningful unsupervised learning in medical imaging, focusing on Alzheimer disease (AD) using b…
AttributeComputational EfficiencyCounterfactual ExplanationData Augmentation+9Language-Guided Trajectory Traversal in Disentangled Stable Diffusion Latent Space for Factorized Medical Image Generation
Text-to-image diffusion models have demonstrated a remarkable ability to generate photorealistic images from natural language prompts. These high-resolution, language-guided synthesized images are essential for the expla…
DiagnosticDisentanglementImage GenerationMedical Image GenerationTowards Interpretable Counterfactual Generation via Multimodal Autoregression
Counterfactual medical image generation enables clinicians to explore clinical hypotheses, such as predicting disease progression, facilitating their decision-making. While existing methods can generate visually plausibl…
counterfactualDecision MakingImage GenerationMedical Image GenerationRL4Med-DDPO: Reinforcement Learning for Controlled Guidance Towards Diverse Medical Image Generation using Vision-Language Foundation Models
Vision-Language Foundation Models (VLFM) have shown a tremendous increase in performance in terms of generating high-resolution, photorealistic natural images. While VLFMs show a rich understanding of semantic content ac…
Image GenerationMedical Image GenerationReinforcement Learning (RL)RELICT: A Replica Detection Framework for Medical Image Generation
Despite the potential of synthetic medical data for augmenting and improving the generalizability of deep learning models, memorization in generative models can lead to unintended leakage of sensitive patient information…
Image GenerationMedical Image GenerationMemorizationDiffusion-Based Approaches in Medical Image Generation and Analysis
Data scarcity in medical imaging poses significant challenges due to privacy concerns. Diffusion models, a recent generative modeling technique, offer a potential solution by generating synthetic and realistic data. Howe…
Image GenerationMedical Image AnalysisMedical Image Generation3D MedDiffusion: A 3D Medical Diffusion Model for Controllable and High-quality Medical Image Generation
The generation of medical images presents significant challenges due to their high-resolution and three-dimensional nature. Existing methods often yield suboptimal performance in generating high-quality 3D medical images…
CT ReconstructionData AugmentationDenoisingImage Generation+2FedCAR: Cross-client Adaptive Re-weighting for Generative Models in Federated Learning
Generative models trained on multi-institutional datasets can provide an enriched understanding through diverse data distributions. However, training the models on medical images is often challenging due to hospitals' re…
Federated LearningImage GenerationMedical Image GenerationPrivacy PreservingMRGen: Diffusion-based Controllable Data Engine for MRI Segmentation towards Unannotated Modalities
Medical image segmentation has recently demonstrated impressive progress with deep neural networks, yet the heterogeneous modalities and scarcity of mask annotations limit the development of segmentation models on unanno…
Image GenerationImage SegmentationMedical Image GenerationMedical Image Segmentation+3IMPROVE: Improving Medical Plausibility without Reliance on HumanValidation -- An Enhanced Prototype-Guided Diffusion Framework
Generative models have proven to be very effective in generating synthetic medical images and find applications in downstream tasks such as enhancing rare disease datasets, long-tailed dataset augmentation, and scaling m…
Image GenerationMedical Image GenerationMediffusion: Joint Diffusion for Self-Explainable Semi-Supervised Classification and Medical Image Generation
We introduce Mediffusion -- a new method for semi-supervised learning with explainable classification based on a joint diffusion model. The medical imaging domain faces unique challenges due to scarce data labelling -- i…
counterfactualImage GenerationMedical Image GenerationExploring Variational Autoencoders for Medical Image Generation: A Comprehensive Study
Variational autoencoder (VAE) is one of the most common techniques in the field of medical image generation, where this architecture has shown advanced researchers in recent years and has developed into various architect…
Data AugmentationDiversityImage GenerationMedical Image GenerationConditional Diffusion Model for Longitudinal Medical Image Generation
Alzheimers disease progresses slowly and involves complex interaction between various biological factors. Longitudinal medical imaging data can capture this progression over time. However, longitudinal data frequently en…
Image GenerationMedical Image GenerationmodelClinical Evaluation of Medical Image Synthesis: A Case Study in Wireless Capsule Endoscopy
Synthetic Data Generation (SDG) based on Artificial Intelligence (AI) can transform the way clinical medicine is delivered by overcoming privacy barriers that currently render clinical data sharing difficult. This is the…
Decision MakingDiagnosticDiversityImage Generation+2Evaluating Utility of Memory Efficient Medical Image Generation: A Study on Lung Nodule Segmentation
The scarcity of publicly available medical imaging data limits the development of effective AI models. This work proposes a memory-efficient patch-wise denoising diffusion probabilistic model (DDPM) for generating synthe…
DenoisingImage GenerationLung Nodule SegmentationMedical Image Generation+1RadGazeGen: Radiomics and Gaze-guided Medical Image Generation using Diffusion Models
In this work, we present RadGazeGen, a novel framework for integrating experts' eye gaze patterns and radiomic feature maps as controls to text-to-image diffusion models for high fidelity medical image generation. Despit…
AnatomyImage GenerationMedical Image Generation