Medical Image Generation
26 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 )
Latest papers
Correction of out-of-focus microscopic images by deep learning
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.
BCI: 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.
Explainable 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.
Overcoming Barriers to Data Sharing with Medical Image Generation: A Comprehensive Evaluation
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.
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.
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.
Image Translation for Medical Image Generation -- Ischemic Stroke Lesions
We demonstrate with the example of ischemic stroke that an improvement in lesion segmentation is feasible using deep learning based augmentation.
Melanoma Detection using Adversarial Training and Deep Transfer Learning
In the first stage, we leverage the inter-class variation of the data distribution for the task of conditional image synthesis by learning the inter-class mapping and synthesizing under-represented class samples from the over-represented ones using unpaired image-to-image translation.
ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction
Current deep neural network based approaches to computed tomography (CT) metal artifact reduction (MAR) are supervised methods that rely on synthesized metal artifacts for training.
Skin Lesion Synthesis with Generative Adversarial Networks
Skin cancer is by far the most common type of cancer.