{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":1,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":17,"n_cols":5,"rows_kept":17,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[4,2,4,3],[5,2,5,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[10,2,10,3],[11,2,11,3],[13,2,13,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNISA996490360903316"]
[null,null,"Titolo",null,"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]"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer International Publishing, , [2022]"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"3-031-16980-8"]
[null,null,"Descrizione fisica",null,"1 online resource (176 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science Ser. ; ; v.13570"]
[null,null,"Disciplina",null,"616.0754"]
[null,null,"Soggetti",null,"Diagnostic imaging - Data processing"]
[{"$nulls":4},"Diagnostic imaging - Digital techniques"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"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 "]
[{"$nulls":4},"Anomalies -- 3 Experiments and Results -- 3.1 COVID-19 Detection -- 3.2 Chest X-ray 14 Diseases Detection -- 3.3 Migraine Detection -- 4 Conclusion -- A  Implementation Details -- B  Network Architectures -- B.1  Discriminator -- B.2  Generator -- References -- Bi-directional Synthesis of Pre- and Post-contrast MRI via Guided Feature Disentanglement -- 1 Introduction -- 2 Methodology -- 3 Experiments and Results."]
[{"$nulls":4},"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."]
[{"$nulls":4},"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."]