Papers Medical Image Generation
“Medical Image Generation” 태그가 달린 논문 97편 · 필터 해제
Devil is in Details: Locality-Aware 3D Abdominal CT Volume Generation for Self-Supervised Organ Segmentation
In the realm of medical image analysis, self-supervised learning (SSL) techniques have emerged to alleviate labeling demands, while still facing the challenge of training data scarcity owing to escalating resource requir…
Image GenerationMedical Image AnalysisMedical Image GenerationIntroducing SDICE: An Index for Assessing Diversity of Synthetic Medical Datasets
Advancements in generative modeling are pushing the state-of-the-art in synthetic medical image generation. These synthetic images can serve as an effective data augmentation method to aid the development of more accurat…
Data AugmentationDiversityImage GenerationMedical Image Analysis+1Kvasir-VQA: A Text-Image Pair GI Tract Dataset
We introduce Kvasir-VQA, an extended dataset derived from the HyperKvasir and Kvasir-Instrument datasets, augmented with question-and-answer annotations to facilitate advanced machine learning tasks in Gastrointestinal (…
Image CaptioningImage GenerationMedical Image AnalysisMedical Image Generation+6MedDiT: A Knowledge-Controlled Diffusion Transformer Framework for Dynamic Medical Image Generation in Virtual Simulated Patient
Medical education relies heavily on Simulated Patients (SPs) to provide a safe environment for students to practice clinical skills, including medical image analysis. However, the high cost of recruiting qualified SPs an…
DiagnosticHallucinationImage GenerationKnowledge Graphs+2On Differentially Private 3D Medical Image Synthesis with Controllable Latent Diffusion Models
Generally, the small size of public medical imaging datasets coupled with stringent privacy concerns, hampers the advancement of data-hungry deep learning models in medical imaging. This study addresses these challenges …
Image GenerationMedical Image GenerationEnhancing Label-efficient Medical Image Segmentation with Text-guided Diffusion Models
Aside from offering state-of-the-art performance in medical image generation, denoising diffusion probabilistic models (DPM) can also serve as a representation learner to capture semantic information and potentially be u…
DenoisingDiagnosticImage GenerationImage Segmentation+4Soft Masked Mamba Diffusion Model for CT to MRI Conversion
Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are the predominant modalities utilized in the field of medical imaging. Although MRI capture the complexity of anatomical structures with greater detail than…
Computed Tomography (CT)Image GenerationMambaMedical Image GenerationRapid Review of Generative AI in Smart Medical Applications
With the continuous advancement of technology, artificial intelligence has significantly impacted various fields, particularly healthcare. Generative models, a key AI technology, have revolutionized medical image generat…
DiagnosticImage GenerationMedical Image GenerationGANetic Loss for Generative Adversarial Networks with a Focus on Medical Applications
Generative adversarial networks (GANs) are machine learning models that are used to estimate the underlying statistical structure of a given dataset and as a result can be used for a variety of tasks such as image genera…
Anomaly DetectionImage GenerationMedical Image GenerationMemControl: Mitigating Memorization in Diffusion Models via Automated Parameter Selection
Diffusion models excel in generating images that closely resemble their training data but are also susceptible to data memorization, raising privacy, ethical, and legal concerns, particularly in sensitive domains such as…
Image GenerationMedical Image GenerationMemorizationparameter-efficient fine-tuningUWAFA-GAN: Ultra-Wide-Angle Fluorescein Angiography Transformation via Multi-scale Generation and Registration Enhancement
Fundus photography, in combination with the ultra-wide-angle fundus (UWF) techniques, becomes an indispensable diagnostic tool in clinical settings by offering a more comprehensive view of the retina. Nonetheless, UWF fl…
DiagnosticGenerative Adversarial NetworkImage GenerationMedical Image GenerationData-Efficient Unsupervised Interpolation Without Any Intermediate Frame for 4D Medical Images
4D medical images, which represent 3D images with temporal information, are crucial in clinical practice for capturing dynamic changes and monitoring long-term disease progression. However, acquiring 4D medical images po…
3D Video Frame InterpolationMedical Image GenerationUnsupervised Video Frame InterpolationVision-Language Synthetic Data Enhances Echocardiography Downstream Tasks
High-quality, large-scale data is essential for robust deep learning models in medical applications, particularly ultrasound image analysis. Diffusion models facilitate high-fidelity medical image generation, reducing th…
Image GenerationMedical Image GenerationSafeguarding Medical Image Segmentation Datasets against Unauthorized Training via Contour- and Texture-Aware Perturbations
The widespread availability of publicly accessible medical images has significantly propelled advancements in various research and clinical fields. Nonetheless, concerns regarding unauthorized training of AI systems for …
image-classificationImage ClassificationImage GenerationImage Segmentation+3An Ordinal Diffusion Model for Generating Medical Images with Different Severity Levels
Diffusion models have recently been used for medical image generation because of their high image quality. In this study, we focus on generating medical images with ordinal classes, which have ordinal relationships, such…
Image GenerationMedical Image GenerationWDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image Synthesis
Due to the three-dimensional nature of CT- or MR-scans, generative modeling of medical images is a particularly challenging task. Existing approaches mostly apply patch-wise, slice-wise, or cascaded generation techniques…
DiversityGPUImage GenerationMedical Image Generation+1Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion Models
Diffusion models have enabled remarkably high-quality medical image generation, yet it is challenging to enforce anatomical constraints in generated images. To this end, we propose a diffusion model-based method that sup…
counterfactualImage GenerationMedical Image GenerationHigh-Quality Medical Image Generation from Free-hand Sketch
Generating medical images from human-drawn free-hand sketches holds promise for various important medical imaging applications. Due to the extreme difficulty in collecting free-hand sketch data in the medical domain, mos…
Image GenerationMedical Image GenerationGAN-GA: A Generative Model based on Genetic Algorithm for Medical Image Generation
Medical imaging is an essential tool for diagnosing and treating diseases. However, lacking medical images can lead to inaccurate diagnoses and ineffective treatments. Generative models offer a promising solution for add…
Data AugmentationDiversityImage GenerationMedical Image GenerationFeature Extraction for Generative Medical Imaging Evaluation: New Evidence Against an Evolving Trend
Fr\'echet Inception Distance (FID) is a widely used metric for assessing synthetic image quality. It relies on an ImageNet-based feature extractor, making its applicability to medical imaging unclear. A recent trend is t…
Data AugmentationMedical Image Generation