Papers Medical Image Generation
“Medical Image Generation” 태그가 달린 논문 97편 · 필터 해제
BiomedJourney: Counterfactual Biomedical Image Generation by Instruction-Learning from Multimodal Patient Journeys
Rapid progress has been made in instruction-learning for image editing with natural-language instruction, as exemplified by InstructPix2Pix. In biomedicine, such methods can be applied to counterfactual image generation,…
counterfactualDenoisingImage GenerationMedical Image GenerationArbitrary Distributions Mapping via SyMOT-Flow: A Flow-based Approach Integrating Maximum Mean Discrepancy and Optimal Transport
Finding a transformation between two unknown probability distributions from finite samples is crucial for modeling complex data distributions and performing tasks such as sample generation, domain adaptation and statisti…
Density EstimationDomain AdaptationImage GenerationMedical Image GenerationUWAT-GAN: Fundus Fluorescein Angiography Synthesis via Ultra-wide-angle Transformation Multi-scale GAN
Fundus photography is an essential examination for clinical and differential diagnosis of fundus diseases. Recently, Ultra-Wide-angle Fundus (UWF) techniques, UWF Fluorescein Angiography (UWF-FA) and UWF Scanning Laser O…
DecoderGenerative Adversarial NetworkImage GenerationMedical Image GenerationGenerateCT: Text-Conditional Generation of 3D Chest CT Volumes
GenerateCT, the first approach to generating 3D medical imaging conditioned on free-form medical text prompts, incorporates a text encoder and three key components: a novel causal vision transformer for encoding 3D CT vo…
Computed Tomography (CT)Image GenerationLanguage ModellingLarge Language Model+3Medical diffusion on a budget: Textual Inversion for medical image generation
Diffusion models for text-to-image generation, known for their efficiency, accessibility, and quality, have gained popularity. While inference with these systems on consumer-grade GPUs is increasingly feasible, training …
Diagnosticdomain classificationImage GenerationMedical Image Generation+2Unsupervised Domain Transfer with Conditional Invertible Neural Networks
Synthetic medical image generation has evolved as a key technique for neural network training and validation. A core challenge, however, remains in the domain gap between simulations and real data. While deep learning-ba…
Image GenerationMedical Image GenerationSynthetic Data GenerationCurrent State of Community-Driven Radiological AI Deployment in Medical Imaging
Artificial Intelligence (AI) has become commonplace to solve routine everyday tasks. Because of the exponential growth in medical imaging data volume and complexity, the workload on radiologists is steadily increasing. W…
Image GenerationMedical Image GenerationSADM: Sequence-Aware Diffusion Model for Longitudinal Medical Image Generation
Human organs constantly undergo anatomical changes due to a complex mix of short-term (e.g., heartbeat) and long-term (e.g., aging) factors. Evidently, prior knowledge of these factors will be beneficial when modeling th…
Image GenerationMedical Image GenerationMedical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation
Recent advances in computer vision have shown promising results in image generation. Diffusion probabilistic models in particular have generated realistic images from textual input, as demonstrated by DALL-E 2, Imagen an…
Computed Tomography (CT)DenoisingImage GenerationMedical Image Generation+1Evaluating the Performance of StyleGAN2-ADA on Medical Images
Although generative adversarial networks (GANs) have shown promise in medical imaging, they have four main limitations that impeded their utility: computational cost, data requirements, reliable evaluation measures, and …
Computed Tomography (CT)Data AugmentationMedical Image GenerationTransfer LearningInflating 2D Convolution Weights for Efficient Generation of 3D Medical Images
The generation of three-dimensional (3D) medical images has great application potential since it takes into account the 3D anatomical structure. Two problems prevent effective training of a 3D medical generative model: (…
Image GenerationMedical Image GenerationBackdoor Attack is a Devil in Federated GAN-based Medical Image Synthesis
Deep Learning-based image synthesis techniques have been applied in healthcare research for generating medical images to support open research. Training generative adversarial neural networks (GAN) usually requires large…
Backdoor AttackData PoisoningFederated LearningImage Generation+2Diffusion Deformable Model for 4D Temporal Medical Image Generation
Temporal volume images with 3D+t (4D) information are often used in medical imaging to statistically analyze temporal dynamics or capture disease progression. Although deep-learning-based generative models for natural im…
DenoisingImage GenerationMedical Image GenerationGeneration of Artificial CT Images using Patch-based Conditional Generative Adversarial Networks
Deep learning has a great potential to alleviate diagnosis and prognosis for various clinical procedures. However, the lack of a sufficient number of medical images is the most common obstacle in conducting image-based a…
Computed Tomography (CT)Data AugmentationImage GenerationMedical Image Generation+1Correction of out-of-focus microscopic images by deep learning
Motivation Microscopic images are widely used in basic biomedical research, disease diagnosis and medical discovery. Obtaining high-quality in-focus microscopy images has been a cornerstone of the microscopy. However, i…
Deep LearningGenerative Adversarial NetworkImage DeblurringMedical Image GenerationBCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix
The evaluation of human epidermal growth factor receptor 2 (HER2) expression is essential to formulate a precise treatment for breast cancer. The routine evaluation of HER2 is conducted with immunohistochemical technique…
Breast Cancer DetectionBreast Cancer Histology Image ClassificationClassification Of Breast Cancer Histology ImagesImage Generation+4Robust deep learning for eye fundus images: Bridging real and synthetic data for enhancing generalization
Deep learning applications for assessing medical images are limited because the datasets are often small and imbalanced. The use of synthetic data has been proposed in the literature, but neither a robust comparison of t…
Data AugmentationGenerative Adversarial NetworkImage GenerationImage Quality Assessment+1Explainable Diabetic Retinopathy Detection and Retinal Image Generation
Though deep learning has shown successful performance in classifying the label and severity stage of certain diseases, most of them give few explanations on how to make predictions. Inspired by Koch's Postulates, the fou…
Data AugmentationDiabetic Retinopathy DetectionImage GenerationInterpretable Machine Learning+2GANs for Medical Image Synthesis: An Empirical Study
Generative Adversarial Networks (GANs) have become increasingly powerful, generating mind-blowing photorealistic images that mimic the content of datasets they were trained to replicate. One recurrent theme in medical im…
Image GenerationMedical Image GenerationConditional Generation of Medical Images via Disentangled Adversarial Inference
Synthetic medical image generation has a huge potential for improving healthcare through many applications, from data augmentation for training machine learning systems to preserving patient privacy. Conditional Adversar…
Data AugmentationDisentanglementImage GenerationMedical Image Generation