Simulation and Synthesis in Medical Imaging: 4th International Workshop, SASHIMI 2019, Held in Conju
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- Artikel-Nr.: 10395466
Beschreibung
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 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.
Eigenschaften
Breite: | 154 |
Gewicht: | 272 g |
Höhe: | 233 |
Länge: | 12 |
Seiten: | 162 |
Sprachen: | Englisch |
Autor: | Ali Gooya, David Svoboda, Ninon Burgos |
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