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
Most implemented papers
Evaluating the Clinical Realism of Synthetic Chest X-Rays Generated Using Progressively Growing GANs
We apply a PGGAN to the task of unsupervised x-ray synthesis and have radiologists evaluate the clinical realism of the resultant samples.
MammoGANesis: Controlled Generation of High-Resolution Mammograms for Radiology Education
During their formative years, radiology trainees are required to interpret hundreds of mammograms per month, with the objective of becoming apt at discerning the subtle patterns differentiating benign from malignant lesions.
Overcoming Barriers to Data Sharing with Medical Image Generation: A Comprehensive Evaluation
AugustDS/synthetic-medical-benchmark
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Our study offers valuable guidelines and outlines practical conditions under which insights derived from synthetic medical images are similar to those that would have been derived from real imaging data.
Explainable Diabetic Retinopathy Detection and Retinal Image Generation
zzdyyy/Patho-GAN
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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.
Robust deep learning for eye fundus images: Bridging real and synthetic data for enhancing generalization
NVlabs/stylegan2-ada-pytorch
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We employed the STARE dataset for external validation, ensuring a comprehensive assessment of the proposed approach.
BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix
bupt-ai-cz/BCI
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The evaluation of human epidermal growth factor receptor 2 (HER2) expression is essential to formulate a precise treatment for breast cancer.
Correction of out-of-focus microscopic images by deep learning
jiangdat/COMI
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Computational and Structural Biotechnology Journal 2022
Results To solve the out-of-focus issue in microscopy, we developed a Cycle Generative Adversarial Network (CycleGAN) based model and a multi-component weighted loss function.
Generation of Artificial CT Images using Patch-based Conditional Generative Adversarial Networks
mhabijan/medical_images_generation
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Deep learning has a great potential to alleviate diagnosis and prognosis for various clinical procedures.
Diffusion Deformable Model for 4D Temporal Medical Image Generation
torchddm/ddm
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Our proposed DDM is composed of the diffusion and the deformation modules so that DDM can learn spatial deformation information between the source and target volumes and provide a latent code for generating intermediate frames along a geodesic path.
Backdoor Attack is a Devil in Federated GAN-based Medical Image Synthesis
nanboy-ronan/backdoor-fedgan
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In this study, we propose a way of attacking federated GAN (FedGAN) by treating the discriminator with a commonly used data poisoning strategy in backdoor attack classification models.
