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Simulation and Synthesis in Medical Imaging [[electronic resource] ] : 8th International Workshop, SASHIMI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings / / edited by Jelmer M. Wolterink, David Svoboda, Can Zhao, Virginia Fernandez
| Simulation and Synthesis in Medical Imaging [[electronic resource] ] : 8th International Workshop, SASHIMI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings / / edited by Jelmer M. Wolterink, David Svoboda, Can Zhao, Virginia Fernandez |
| Autore | Wolterink Jelmer M |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (151 pages) |
| Disciplina | 006.37 |
| Altri autori (Persone) |
SvobodaDavid
ZhaoCan FernandezVirginia |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Computer vision
Artificial intelligence Education - Data processing Application software Computer Vision Artificial Intelligence Computers and Education Computer and Information Systems Applications |
| ISBN | 3-031-44689-5 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Transformers for CT Reconstruction From Monoplanar and Biplanar Radiographs Super-resolution Segmentation network for inner-ear tissue segmentation -- Multi-Phase Liver-Specific DCE-MRI Translation via a Registration-Guided GAN -- Learned Local Attention Maps for Synthesising Vessel Segmentations from T2 MRI Physics-Aware Motion Simulation for T2*-Weighted Brain MRI -- Unsupervised heteromodal physics-informed representation of MRI data: tackling data harmonisation, imputation and domain shift -- TAI-GAN: Temporally and Anatomically Informed GAN for early-to-late frame conversion in dynamic cardiac PET motion correction -- How Good Are Synthetic Medical Images? An Empirical Study with Lung Ultrasound Unsupervised Liver Tumor Segmentation with Pseudo Anomaly Synthesis -- Improving style transfer in dynamic contrast enhanced MRI using a spatio-temporal approach Synthetic Singleplex-Image Generation in Multiplex-Brightfield Immunohistochemistry -- Digital Pathology using Deep Generative Models Self-Supervised Super-Resolution for Anisotropic MR Images with and without Slice Gap DIFF·3: A latent diffusion model for the generation of synthetic 3D echocardiographic images and corresponding labels. |
| Record Nr. | UNISA-996558469903316 |
Wolterink Jelmer M
|
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Simulation and Synthesis in Medical Imaging : 8th International Workshop, SASHIMI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings / / edited by Jelmer M. Wolterink, David Svoboda, Can Zhao, Virginia Fernandez
| Simulation and Synthesis in Medical Imaging : 8th International Workshop, SASHIMI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings / / edited by Jelmer M. Wolterink, David Svoboda, Can Zhao, Virginia Fernandez |
| Autore | Wolterink Jelmer M |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (151 pages) |
| Disciplina | 006.37 |
| Altri autori (Persone) |
SvobodaDavid
ZhaoCan FernándezVirginia |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Computer vision
Artificial intelligence Education - Data processing Application software Computer Vision Artificial Intelligence Computers and Education Computer and Information Systems Applications Imatges mèdiques Visió per ordinador Intel·ligència artificial Interfícies de programació d'aplicacions Sistemes informàtics |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN |
9783031446894
3031446895 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Transformers for CT Reconstruction From Monoplanar and Biplanar Radiographs Super-resolution Segmentation network for inner-ear tissue segmentation -- Multi-Phase Liver-Specific DCE-MRI Translation via a Registration-Guided GAN -- Learned Local Attention Maps for Synthesising Vessel Segmentations from T2 MRI Physics-Aware Motion Simulation for T2*-Weighted Brain MRI -- Unsupervised heteromodal physics-informed representation of MRI data: tackling data harmonisation, imputation and domain shift -- TAI-GAN: Temporally and Anatomically Informed GAN for early-to-late frame conversion in dynamic cardiac PET motion correction -- How Good Are Synthetic Medical Images? An Empirical Study with Lung Ultrasound Unsupervised Liver Tumor Segmentation with Pseudo Anomaly Synthesis -- Improving style transfer in dynamic contrast enhanced MRI using a spatio-temporal approach Synthetic Singleplex-Image Generation in Multiplex-Brightfield Immunohistochemistry -- Digital Pathology using Deep Generative Models Self-Supervised Super-Resolution for Anisotropic MR Images with and without Slice Gap DIFF·3: A latent diffusion model for the generation of synthetic 3D echocardiographic images and corresponding labels. |
| Record Nr. | UNINA-9910747597003321 |
|
Wolterink Jelmer M
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Simulation and synthesis in medical imaging : 7th international workshop, SASHIMI 2022, held in conjunction with MICCAI 2022, Singapore, September 18, 2022 : proceedings / / Can Zhao [and three others]
| Simulation and synthesis in medical imaging : 7th international workshop, SASHIMI 2022, held in conjunction with MICCAI 2022, Singapore, September 18, 2022 : proceedings / / Can Zhao [and three others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer International Publishing, , [2022] |
| Descrizione fisica | 1 online resource (176 pages) |
| Disciplina | 616.0754 |
| Collana | Lecture Notes in Computer Science Ser. |
| Soggetto topico |
Diagnostic imaging - Data processing
Diagnostic imaging - Digital techniques |
| ISBN | 3-031-16980-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Subject-Specific Lesion Generation and Pseudo-Healthy Synthesis for Multiple Sclerosis Brain Images -- 1 Introduction -- 1.1 Related Works -- 1.2 Contributions -- 2 Methods -- 2.1 Generators -- 2.2 Discriminators -- 2.3 Losses -- 3 Experiments -- 3.1 Evaluation -- 3.2 Implementation -- 3.3 Data -- 3.4 Results -- 4 Conclusion -- References -- Generating Artificial Artifacts for Motion Artifact Detection in Chest CT -- 1 Introduction -- 2 Methods -- 3 Experiments -- 4 Results -- 5 Discussion -- References -- Probabilistic Image Diversification to Improve Segmentation in 3D Microscopy Image Data -- 1 Introduction -- 2 Probabilistic Image Diversification -- 3 Experiments and Results -- 3.1 Data Augmentation -- 3.2 Benchmarking -- 3.3 Test-Time Augmentation -- 4 Discussion and Conclusion -- References -- Pathology Synthesis of 3D Consistent Cardiac MR Images Using 2D VAEs and GANs -- 1 Introduction -- 1.1 Contributions -- 2 Method -- 2.1 Pathology Synthesis -- 2.2 Modeling Slice Relationship -- 2.3 Data and Implementation -- 3 Results -- 3.1 Pathology Synthesis -- 3.2 Modeling the Slice Relationship -- 4 Discussion and Conclusion -- References -- .26em plus .1em minus .1emHealthyGAN: Learning from Unannotated Medical Images to Detect Anomalies Associated with Human Disease -- 1 Introduction -- 2 HealthyGAN: The Proposed Method -- 2.1 Network Architecture -- 2.2 Training -- 2.3 Detecting Anomalies -- 3 Experiments and Results -- 3.1 COVID-19 Detection -- 3.2 Chest X-ray 14 Diseases Detection -- 3.3 Migraine Detection -- 4 Conclusion -- A Implementation Details -- B Network Architectures -- B.1 Discriminator -- B.2 Generator -- References -- Bi-directional Synthesis of Pre- and Post-contrast MRI via Guided Feature Disentanglement -- 1 Introduction -- 2 Methodology -- 3 Experiments and Results.
4 Conclusion -- References -- Morphology-Preserving Autoregressive 3D Generative Modelling of the Brain -- 1 Introduction -- 2 Background -- 2.1 VQ-VAE -- 2.2 Transformer -- 3 Methods -- 3.1 Descriptive Quantization for Transformer Usage -- 3.2 Autoregressive Modelling of the Brain -- 4 Experiments and Results -- 4.1 Quantitative Image Fidelity Evaluation -- 4.2 Morphological Evaluation -- 5 Conclusion -- 6 Appendix -- 6.1 VQ-VAEs -- 6.2 Transformers -- 6.3 Losses -- 6.4 Datasets -- 6.5 VBM Analysis -- References -- Can Segmentation Models Be Trained with Fully Synthetically Generated Data? -- 1 Background -- 2 Materials and Methods -- 2.1 Materials -- 2.2 Methods -- 2.3 Segmentation Network Used for the Experiments -- 3 Experiments -- 3.1 Can We Learn to Segment Healthy Regions Using Synthetic Data? -- 3.2 Can Synthetic Generative Models Address Out-of-Distribution Segmentation? -- 3.3 Can We Learn to Segment Pathologies from Synthetic Data? -- 4 Discussion and Conclusion -- A Training Set-Ups -- A.1 Training brainSPADE -- A.2 Training Segmentation nnU-Nets -- B Additional Figures -- References -- Multimodal Super Resolution with Dual Domain Loss and Gradient Guidance -- 1 Introduction -- 2 Materials and Methods -- 3 Experiments -- 4 Discussion and Conclusion -- References -- Brain Lesion Synthesis via Progressive Adversarial Variational Auto-Encoder -- 1 Introduction -- 2 Methodology -- 2.1 Model Architecture -- 2.2 Condition and Mask Embedding Blocks -- 2.3 Loss Functions -- 3 Experiments -- 3.1 Dataset -- 3.2 Implementation Details -- 3.3 Evaluation Metrics -- 3.4 Experimental Results -- 4 Discussion and Conclusion -- References -- Contrastive Learning for Generating Optical Coherence Tomography Images of the Retina -- 1 Introduction -- 2 Related Work -- 3 Methods -- 4 Experiments and Results -- 4.1 Dataset -- 4.2 Model Training. 4.3 Results -- 5 Conclusions -- References -- A Novel Method Combining Global and Local Assessments to Evaluate CBCT-Based Synthetic CTs -- 1 Introduction -- 2 Methods -- 2.1 Data Acquisition and Processing -- 2.2 Validation Methodology -- 3 Results -- 3.1 Validation Methodology -- 4 Discussion -- 5 Conclusion -- References -- SuperFormer: Volumetric Transformer Architectures for MRI Super-Resolution -- 1 Introduction -- 2 Method -- 2.1 Feature Embedding -- 2.2 Volume Embedding -- 2.3 3D Deep Feature Extraction -- 2.4 HQ Volume Reconstruction -- 3 Experimental Setup -- 3.1 Implementation Details -- 3.2 Results -- 4 Conclusion -- References -- Evaluating the Performance of StyleGAN2-ADA on Medical Images -- 1 Introduction -- 2 Methods -- 2.1 Data -- 2.2 Generative Modeling -- 2.3 Evaluation Measures -- 3 Results -- 4 Conclusion -- References -- Backdoor Attack is a Devil in Federated GAN-Based Medical Image Synthesis -- 1 Introduction -- 2 Methods -- 2.1 Federated Generative Adversarial Network -- 2.2 Backdoor Attack Strategies -- 2.3 Defense Strategies -- 3 Experiments -- 3.1 Experimental Settings -- 3.2 Implementation of Attack -- 3.3 Implementation of Defense -- 3.4 Results and Discussion -- 4 Conclusion -- A More Experiment Results -- B WGAN-GP with Large Trigger Size -- References -- Author Index. |
| Record Nr. | UNISA-996490360903316 |
| Cham, Switzerland : , : Springer International Publishing, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Simulation and Synthesis in Medical Imaging : 7th International Workshop, SASHIMI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings / / edited by Can Zhao, David Svoboda, Jelmer M. Wolterink, Maria Escobar
| Simulation and Synthesis in Medical Imaging : 7th International Workshop, SASHIMI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings / / edited by Can Zhao, David Svoboda, Jelmer M. Wolterink, Maria Escobar |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (176 pages) |
| Disciplina | 616.0754 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Computer vision
Artificial intelligence Education - Data processing Application software Computer Vision Artificial Intelligence Computers and Education Computer and Information Systems Applications Diagnòstic per la imatge Visió per ordinador Intel·ligència artificial en medicina |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN |
9783031169809
3031169808 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Subject-Specific Lesion Generation and Pseudo-Healthy Synthesis for Multiple Sclerosis Brain Images -- 1 Introduction -- 1.1 Related Works -- 1.2 Contributions -- 2 Methods -- 2.1 Generators -- 2.2 Discriminators -- 2.3 Losses -- 3 Experiments -- 3.1 Evaluation -- 3.2 Implementation -- 3.3 Data -- 3.4 Results -- 4 Conclusion -- References -- Generating Artificial Artifacts for Motion Artifact Detection in Chest CT -- 1 Introduction -- 2 Methods -- 3 Experiments -- 4 Results -- 5 Discussion -- References -- Probabilistic Image Diversification to Improve Segmentation in 3D Microscopy Image Data -- 1 Introduction -- 2 Probabilistic Image Diversification -- 3 Experiments and Results -- 3.1 Data Augmentation -- 3.2 Benchmarking -- 3.3 Test-Time Augmentation -- 4 Discussion and Conclusion -- References -- Pathology Synthesis of 3D Consistent Cardiac MR Images Using 2D VAEs and GANs -- 1 Introduction -- 1.1 Contributions -- 2 Method -- 2.1 Pathology Synthesis -- 2.2 Modeling Slice Relationship -- 2.3 Data and Implementation -- 3 Results -- 3.1 Pathology Synthesis -- 3.2 Modeling the Slice Relationship -- 4 Discussion and Conclusion -- References -- .26em plus .1em minus .1emHealthyGAN: Learning from Unannotated Medical Images to Detect Anomalies Associated with Human Disease -- 1 Introduction -- 2 HealthyGAN: The Proposed Method -- 2.1 Network Architecture -- 2.2 Training -- 2.3 Detecting Anomalies -- 3 Experiments and Results -- 3.1 COVID-19 Detection -- 3.2 Chest X-ray 14 Diseases Detection -- 3.3 Migraine Detection -- 4 Conclusion -- A Implementation Details -- B Network Architectures -- B.1 Discriminator -- B.2 Generator -- References -- Bi-directional Synthesis of Pre- and Post-contrast MRI via Guided Feature Disentanglement -- 1 Introduction -- 2 Methodology -- 3 Experiments and Results.
4 Conclusion -- References -- Morphology-Preserving Autoregressive 3D Generative Modelling of the Brain -- 1 Introduction -- 2 Background -- 2.1 VQ-VAE -- 2.2 Transformer -- 3 Methods -- 3.1 Descriptive Quantization for Transformer Usage -- 3.2 Autoregressive Modelling of the Brain -- 4 Experiments and Results -- 4.1 Quantitative Image Fidelity Evaluation -- 4.2 Morphological Evaluation -- 5 Conclusion -- 6 Appendix -- 6.1 VQ-VAEs -- 6.2 Transformers -- 6.3 Losses -- 6.4 Datasets -- 6.5 VBM Analysis -- References -- Can Segmentation Models Be Trained with Fully Synthetically Generated Data? -- 1 Background -- 2 Materials and Methods -- 2.1 Materials -- 2.2 Methods -- 2.3 Segmentation Network Used for the Experiments -- 3 Experiments -- 3.1 Can We Learn to Segment Healthy Regions Using Synthetic Data? -- 3.2 Can Synthetic Generative Models Address Out-of-Distribution Segmentation? -- 3.3 Can We Learn to Segment Pathologies from Synthetic Data? -- 4 Discussion and Conclusion -- A Training Set-Ups -- A.1 Training brainSPADE -- A.2 Training Segmentation nnU-Nets -- B Additional Figures -- References -- Multimodal Super Resolution with Dual Domain Loss and Gradient Guidance -- 1 Introduction -- 2 Materials and Methods -- 3 Experiments -- 4 Discussion and Conclusion -- References -- Brain Lesion Synthesis via Progressive Adversarial Variational Auto-Encoder -- 1 Introduction -- 2 Methodology -- 2.1 Model Architecture -- 2.2 Condition and Mask Embedding Blocks -- 2.3 Loss Functions -- 3 Experiments -- 3.1 Dataset -- 3.2 Implementation Details -- 3.3 Evaluation Metrics -- 3.4 Experimental Results -- 4 Discussion and Conclusion -- References -- Contrastive Learning for Generating Optical Coherence Tomography Images of the Retina -- 1 Introduction -- 2 Related Work -- 3 Methods -- 4 Experiments and Results -- 4.1 Dataset -- 4.2 Model Training. 4.3 Results -- 5 Conclusions -- References -- A Novel Method Combining Global and Local Assessments to Evaluate CBCT-Based Synthetic CTs -- 1 Introduction -- 2 Methods -- 2.1 Data Acquisition and Processing -- 2.2 Validation Methodology -- 3 Results -- 3.1 Validation Methodology -- 4 Discussion -- 5 Conclusion -- References -- SuperFormer: Volumetric Transformer Architectures for MRI Super-Resolution -- 1 Introduction -- 2 Method -- 2.1 Feature Embedding -- 2.2 Volume Embedding -- 2.3 3D Deep Feature Extraction -- 2.4 HQ Volume Reconstruction -- 3 Experimental Setup -- 3.1 Implementation Details -- 3.2 Results -- 4 Conclusion -- References -- Evaluating the Performance of StyleGAN2-ADA on Medical Images -- 1 Introduction -- 2 Methods -- 2.1 Data -- 2.2 Generative Modeling -- 2.3 Evaluation Measures -- 3 Results -- 4 Conclusion -- References -- Backdoor Attack is a Devil in Federated GAN-Based Medical Image Synthesis -- 1 Introduction -- 2 Methods -- 2.1 Federated Generative Adversarial Network -- 2.2 Backdoor Attack Strategies -- 2.3 Defense Strategies -- 3 Experiments -- 3.1 Experimental Settings -- 3.2 Implementation of Attack -- 3.3 Implementation of Defense -- 3.4 Results and Discussion -- 4 Conclusion -- A More Experiment Results -- B WGAN-GP with Large Trigger Size -- References -- Author Index. |
| Record Nr. | UNINA-9910595037403321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||