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_aSimulation and Synthesis in Medical Imaging _h[electronic resource] : _b4th International Workshop, SASHIMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings / _cedited by Ninon Burgos, Ali Gooya, David Svoboda. |
250 | _a1st ed. 2019. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2019. |
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300 |
_aX, 162 p. 78 illus., 60 illus. in color. _bonline resource. |
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490 | 1 |
_aImage Processing, Computer Vision, Pattern Recognition, and Graphics, _x3004-9954 ; _v11827 |
|
505 | 0 | _aEmpirical 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. | |
520 | _aThis book constitutes the refereed proceedings of the 4th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. The 16 full papers presented were carefully reviewed and selected from 21 submissions. The contributions span the following broad categories in alignment with the initial call-for-papers: methods based on generative models or adversarial learning for MRI/CT/PET/microscopy image synthesis, image super resolution, and several applications of image synthesis and simulation for data augmentation, segmentation or lesion detection. | ||
650 | 0 |
_aComputer vision. _984876 |
|
650 | 0 |
_aArtificial intelligence. _93407 |
|
650 | 0 |
_aMedical informatics. _94729 |
|
650 | 0 |
_aComputer science _xMathematics. _93866 |
|
650 | 1 | 4 |
_aComputer Vision. _984878 |
650 | 2 | 4 |
_aArtificial Intelligence. _93407 |
650 | 2 | 4 |
_aHealth Informatics. _931799 |
650 | 2 | 4 |
_aMathematics of Computing. _931875 |
700 | 1 |
_aBurgos, Ninon. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _984879 |
|
700 | 1 |
_aGooya, Ali. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _984882 |
|
700 | 1 |
_aSvoboda, David. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _984883 |
|
710 | 2 |
_aSpringerLink (Online service) _984886 |
|
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783030327774 |
776 | 0 | 8 |
_iPrinted edition: _z9783030327798 |
830 | 0 |
_aImage Processing, Computer Vision, Pattern Recognition, and Graphics, _x3004-9954 ; _v11827 _984887 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-030-32778-1 |
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