Simulation and Synthesis in Medical Imaging

<p>Empirical Bayesian Mixture Models for Medical Image Translation.- Improved MR to CT synthesis for PET/MR attenuation correction using Imitation Learning.- Unpaired Multi-Contrast MR Image Synthesis using Generative Adversarial Networks.- Unsupervised Retina Image Synthesis via Disentangled Representation Learning.- Pseudo-normal PET Synthesis with Generative Adversarial Networks for Localising Hypometabolism in Epilepsies.- Breast Mass Detection in Mammograms via Blending Adversarial Learning.- Tunable CT lung nodule synthesis conditioned on background image and semantic features.- Mask2Lesion: Mask-Constrained Adversarial Skin Lesion Image Synthesis.- Towards Annotation-Free Segmentation of Fluorescently Labeled Cell Membranes in Confocal&nbsp; Microscopy Images.- Intelligent image synthesis to attack a segmentation CNN using adversarial learning.- Physics-informed brain MRI segmentation.- 3D Medical Image Synthesis by Factorised Representation and Deformable Model Learning.- Cycle-consistent training for Reducing Negative Jacobian Determinant in Deep Registration Networks.- iSMORE: an iterative self super-resolution algorithm.- An Optical Model of Whole Blood for Detecting Platelets in Lens-Free Images.- Evaluation of the realism of an MRI simulator for stroke lesion prediction using convolutional neural network.</p>