Medical Image Generation
29 papers with code • 5 benchmarks • 4 datasets
Medical image generation is the task of synthesising new medical images.
( Image credit: Towards Adversarial Retinal Image Synthesis )
Benchmarks
These leaderboards are used to track progress in Medical Image Generation
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ChestX-ray14
ACDC
BCI
Leishmania parasite dataset
Latest papers with no code
Safeguarding Medical Image Segmentation Datasets against Unauthorized Training via Contour- and Texture-Aware Perturbations
This is particularly true for medical image segmentation (MIS) datasets, where the processes of collection and fine-grained annotation are time-intensive and laborious.
An 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.
High-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.
GAN-GA: A Generative Model based on Genetic Algorithm for Medical Image Generation
The proposed GAN-GA model is tested by generating Acute lymphoblastic leukemia (ALL) medical images, an image dataset, and is the first time to be used in generative models.
BiomedJourney: Counterfactual Biomedical Image Generation by Instruction-Learning from Multimodal Patient Journeys
In a comprehensive battery of tests on counterfactual medical image generation, BiomedJourney substantially outperforms prior state-of-the-art methods in instruction image editing and medical image generation such as InstructPix2Pix and RoentGen.
Arbitrary 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 statistical inference.
Medical diffusion on a budget: textual inversion for medical image generation
In this study, we conducted experiments using medical datasets comprising only 100 samples from three medical modalities.
Unsupervised Domain Transfer with Conditional Invertible Neural Networks
Synthetic medical image generation has evolved as a key technique for neural network training and validation.
Current State of Community-Driven Radiological AI Deployment in Medical Imaging
To address the barrier to clinical deployment, we have formed MONAI Consortium, an open-source community which is building standards for AI deployment in healthcare institutions, and developing tools and infrastructure to facilitate their implementation.
Evaluating the Performance of StyleGAN2-ADA on Medical Images
Our computational ablation study revealed that transfer learning and data augmentation stabilize training and improve the perceptual quality of the generated images.
