Info
Bridging Regulatory Science and Medical Imaging Evaluation; and Distributed, Collaborative, and Federated Learning : First International Workshop, BRIDGE 2025, and 6th International Workshop, DeCaF 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 23 and September 27, 2025, Proceedings
| Bridging Regulatory Science and Medical Imaging Evaluation; and Distributed, Collaborative, and Federated Learning : First International Workshop, BRIDGE 2025, and 6th International Workshop, DeCaF 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 23 and September 27, 2025, Proceedings |
| Autore | Zamzmi Ghada |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham : , : Springer, , 2025 |
| Descrizione fisica | 1 online resource (265 pages) |
| Altri autori (Persone) |
ReinkeAnnika
SamalaRavi JiangMeirui LiXiaoxiao RothHolger SidulovaMariia KooiThijs AlbarqouniShadi BakasSpyridon |
| Collana | Lecture Notes in Computer Science Series |
| ISBN | 3-032-05663-2 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996678676303316 |
Zamzmi Ghada
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| Cham : , : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Distributed, collaborative, and federated learning, and affordable AI and healthcare for resource diverse global health : third MICCAI workshop, FAIR 2022, and third MICCAI workshop, DeCaF 2022, held in conjunction with MICCAI 2022, Singapore, September 18 and 22, 2022, proceedings / / Shadi Albarqouni [and twelve others], editors
| Distributed, collaborative, and federated learning, and affordable AI and healthcare for resource diverse global health : third MICCAI workshop, FAIR 2022, and third MICCAI workshop, DeCaF 2022, held in conjunction with MICCAI 2022, Singapore, September 18 and 22, 2022, proceedings / / Shadi Albarqouni [and twelve others], editors |
| Pubbl/distr/stampa | Cham : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (215 pages) |
| Disciplina | 610.28563 |
| Collana | Lecture notes in computer science |
| Soggetto topico |
Artificial intelligence - Medical applications
Diagnostic imaging - Data processing Machine learning |
| ISBN | 3-031-18523-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface DeCaF 2022 -- Preface FAIR 2022 -- Organization -- Contents -- Distributed, Collaborative, and Federated Learning -- Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation -- 1 Introduction -- 2 Method -- 2.1 Problem Setup -- 2.2 Preliminary -- 2.3 Proposed Incremental Transfer Learning Multi-site Method -- 3 Experiments -- 4 Analysis and Discussion -- 5 Conclusion -- References -- FedAP: Adaptive Personalization in Federated Learning for Non-IID Data -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Definitions -- 3.2 Federated Averaging -- 3.3 Federated Adaptive Personalization -- 3.4 Hierarchical Clustering -- 4 Experiments and Results -- 4.1 Experimental Setup -- 4.2 Results and Discussions -- 5 Conclusion -- References -- Data Stealing Attack on Medical Images: Is It Safe to Export Networks from Data Lakes? -- 1 Introduction -- 2 Data Stealing Attack -- 2.1 Attack Strategy -- 2.2 Attack Implementation -- 3 Experiments -- 3.1 Datasets and Models -- 3.2 Effectiveness of Data Stealing Attacks -- 3.3 Mitigation of Data Stealing Attacks -- 4 Conclusion -- References -- Can Collaborative Learning Be Private, Robust and Scalable? -- 1 Introduction -- 2 Related Work -- 3 Methods -- 4 Experiments -- 4.1 Experimental Setting -- 4.2 Performance Overview -- 4.3 Different Privacy Regimes Under Quantization -- 4.4 Using Adversarial Training -- 4.5 Train- and Inference-Time Attacks -- 5 Discussion and Conclusion -- References -- Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-modal Brain Tumor Segmentation -- 1 Introduction -- 2 Methods -- 2.1 Split-U-Net -- 2.2 Measuring Data Leakage by Inversion Attack -- 2.3 Defenses -- 3 Experiments and Results -- 4 Discussion -- References.
Joint Multi Organ and Tumor Segmentation from Partial Labels Using Federated Learning -- 1 Introduction -- 2 Methods -- 2.1 Federated Learning -- 2.2 Federated Averaging for Learning from Partial Labels -- 3 Experimental Details and Results -- 3.1 Datasets -- 3.2 Implementation Details -- 3.3 Experimental Results -- 3.4 Validation on External Dataset -- 4 Discussion -- 5 Conclusion -- References -- GAN Latent Space Manipulation and Aggregation for Federated Learning in Medical Imaging -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Overview -- 3.2 Generative Adversarial Network -- 3.3 Privacy-Preserving Aggregation -- 4 Experiments and Results -- 4.1 Datasets and Training Procedure -- 4.2 Experimental Results -- 5 Conclusion -- References -- A Specificity-Preserving Generative Model for Federated MRI Translation -- 1 Introduction -- 2 Theory -- 2.1 MRI Translation with Adversarial Models -- 2.2 Specificity-Preserving Federated Learning of MRI Translation -- 3 Methods -- 3.1 Datasets -- 3.2 Competing Methods -- 3.3 Experiments -- 4 Results -- 5 Discussion and Conclusion -- References -- Content-Aware Differential Privacy with Conditional Invertible Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 (Conditional) Invertible Neural Networks -- 3.2 Content-Aware Differential Privacy -- 4 Experiments -- 5 Results -- 6 Discussion and Conclusion -- References -- DeMed: A Novel and Efficient Decentralized Learning Framework for Medical Images Classification on Blockchain -- 1 Introduction -- 2 Preliminary -- 2.1 Blockchain -- 2.2 Self-supervised Learning (SSL) -- 3 Method -- 3.1 Overview of the Framework -- 3.2 Launch Efficient Deep Learning Training on Blockchain -- 3.3 Secure Training on Blockchain with User Selection -- 4 Experiment -- 4.1 Experiment Setup and Datasets -- 4.2 Comparison Between Aggregation Methods. 4.3 Comparison Between Learning Strategies -- 5 Conclusion -- References -- Cluster Based Secure Multi-party Computation in Federated Learning for Histopathology Images -- 1 Introduction -- 2 Method -- 3 Experiments and Results -- 3.1 Datasets -- 3.2 Experimental Details -- 3.3 Results and Discussions -- 4 Conclusions -- References -- Towards More Efficient Data Valuation in Healthcare Federated Learning Using Ensembling-4pt -- 1 Introduction -- 2 Related Work -- 3 Background: SV Computation -- 4 Shapley Value for Federated Learning Using Ensembling -- 5 Experimental Evaluation -- 6 Results -- 7 Conclusion -- References -- Towards Real-World Federated Learning in Medical Image Analysis Using Kaapana -- 1 Introduction -- 2 Related Work -- 3 Methods -- 4 Experiments -- 5 Results -- 6 Discussion -- References -- Towards Sparsified Federated Neuroimaging Models via Weight Pruning -- 1 Introduction -- 2 Neuroimaging Learning Environments -- 3 Model Pruning -- 4 Results -- 5 Discussion -- References -- Affordable AI and Healthcare -- Enhancing Portable OCT Image Quality via GANs for AI-Based Eye Disease Detection -- 1 Introduction -- 1.1 Background and Motivation -- 1.2 Past Work -- 1.3 Baseline Performance on p-OCT Data and Dataset Details -- 2 Super-Resolving p-OCT Data with ESRGAN -- 2.1 ESRGAN Background and Methods -- 2.2 ESRGAN Results and Discussion -- 3 Enhancing Source Domain Perceptual Image Quality with MedGAN -- 3.1 MedGAN Background -- 3.2 MedGAN Methods -- 3.3 MedGAN Results and Discussion -- 4 Conclusions and Future Directions -- References -- Deep Learning-Based Segmentation of Pleural Effusion from Ultrasound Using Coordinate Convolutions -- 1 Introduction -- 2 Materials -- 3 Methods -- 4 Experiments -- 5 Results -- 6 Discussion and Conclusions -- References. Verifiable and Energy Efficient Medical Image Analysis with Quantised Self-attentive Deep Neural Networks -- 1 Introduction -- 2 Method -- 2.1 Stand-Alone Self-attention -- 2.2 Quantisation of Network Parameters -- 2.3 Network Architecture -- 3 Experiments -- 3.1 Datasets -- 3.2 Implementation Details -- 4 Results and Discussions -- 4.1 Qualitative Analysis -- 4.2 Quantitative Analysis -- 4.3 Computational Analysis -- 4.4 Analysis of Clinical Relevance -- 5 Conclusion -- References -- LRH-Net: A Multi-level Knowledge Distillation Approach for Low-Resource Heart Network -- 1 Introduction -- 2 Methodology -- 2.1 Pre-processing -- 2.2 Architecture -- 3 Experiments -- 3.1 Dataset -- 3.2 Implementation Details -- 4 Results and Discussion -- 4.1 Baselines -- 5 Conclusion -- References -- Author Index. |
| Record Nr. | UNISA-996495570603316 |
| Cham : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health : Third MICCAI Workshop, DeCaF 2022, and Second MICCAI Workshop, FAIR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18 and 22, 2022, Proceedings / / edited by Shadi Albarqouni, Spyridon Bakas, Sophia Bano, M. Jorge Cardoso, Bishesh Khanal, Bennett Landman, Xiaoxiao Li, Chen Qin, Islem Rekik, Nicola Rieke, Holger Roth, Debdoot Sheet, Daguang Xu
| Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health : Third MICCAI Workshop, DeCaF 2022, and Second MICCAI Workshop, FAIR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18 and 22, 2022, Proceedings / / edited by Shadi Albarqouni, Spyridon Bakas, Sophia Bano, M. Jorge Cardoso, Bishesh Khanal, Bennett Landman, Xiaoxiao Li, Chen Qin, Islem Rekik, Nicola Rieke, Holger Roth, Debdoot Sheet, Daguang Xu |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (215 pages) |
| Disciplina |
610.28563
616.0757 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Computer vision
Artificial intelligence Computers Application software Computer Vision Artificial Intelligence Computing Milieux Computer and Information Systems Applications |
| ISBN |
9783031185236
3031185234 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Distributed, Collaborative, and Federated Learning -- Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation -- FedAP: Adaptive Personalization in Federated Learning for Non-IID Data Data Stealing Attack on Medical Images: Is it Safe to Export Networks from Data Lakes? -- Data Stealing Attack on Medical Images: Is it Safe to Export Networks from Data Lakes? -- Can collaborative learning be private, robust and scalable? -- Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation -- Joint Multi Organ and Tumor Segmentation from Partial Labels using Federated Learning -- Fuh, Kensaku Mori, Weichung Wang, Holger R Roth GAN Latent Space Manipulation and Aggregation for Federated Learning in Medical Imaging -- A Specificity-Preserving Generative Model for Federated MRI Translation -- Content-Aware Differential Privacy with Conditional Invertible Neural Networks -- DeMed: A Novel and Efficient Decentralized Learning Framework for Medical Images Classification on Blockchain -- Cluster Based Secure Multi-Party Computation in Federated Learning for Histopathology Images -- Towards More Efficient Data Valuation in Healthcare Federated Learning using Ensembling -- Towards Real-World Federated Learning in Medical Image Analysis Using Kaapana -- Towards Sparsified Federated Neuroimaging Models via Weight Pruning -- Affordable AI and Healthcare -- Enhancing Portable OCT Image Quality via GANs for AI-Based Eye Disease Detection -- Deep Learning-based Segmentation of Pleural Effusion From Ultrasound Using Coordinate Convolutions -- Verifiable and Energy Efficient Medical Image Analysis with Quantised Self-attentive Deep Neural Networks -- LRH-Net: A Multi-Level Knowledge Distillation Approach for Low-Resource Heart Network. |
| Record Nr. | UNINA-9910616395003321 |
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data [[electronic resource] ] : First MICCAI Workshop, DART 2019, and First International Workshop, MIL3ID 2019, Shenzhen, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings / / edited by Qian Wang, Fausto Milletari, Hien V. Nguyen, Shadi Albarqouni, M. Jorge Cardoso, Nicola Rieke, Ziyue Xu, Konstantinos Kamnitsas, Vishal Patel, Badri Roysam, Steve Jiang, Kevin Zhou, Khoa Luu, Ngan Le
| Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data [[electronic resource] ] : First MICCAI Workshop, DART 2019, and First International Workshop, MIL3ID 2019, Shenzhen, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings / / edited by Qian Wang, Fausto Milletari, Hien V. Nguyen, Shadi Albarqouni, M. Jorge Cardoso, Nicola Rieke, Ziyue Xu, Konstantinos Kamnitsas, Vishal Patel, Badri Roysam, Steve Jiang, Kevin Zhou, Khoa Luu, Ngan Le |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XVII, 254 p. 113 illus., 79 illus. in color.) |
| Disciplina | 616.07540285 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Optical data processing
Artificial intelligence Health informatics Image Processing and Computer Vision Artificial Intelligence Health Informatics |
| ISBN | 3-030-33391-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | DART 2019 -- Noise as Domain Shift: Denoising Medical Images by Unpaired Image Translation -- Temporal Consistency Objectives Regularize the Learning of Disentangled Representations -- Multi-layer Domain Adaptation for Deep Convolutional Networks -- Intramodality Domain Adaptation using Self Ensembling and Adversarial Training -- Learning Interpretable Disentangled Representations using Adversarial VAEs -- Synthesising Images and Labels Between MR Sequence Types With CycleGAN -- Multi-Domain Adaptation in Brain MRI through Paired Consistency and Adversarial Learning -- Cross-modality Knowledge Transfer for Prostate Segmentation from CT Scans -- A Pulmonary Nodule Detection Method Based on Residual Learning and Dense Connection -- Harmonization and Targeted Feature Dropout for Generalized Segmentation: Application to Multi-site Traumatic Brain Injury Images -- Improving Pathological Structure Segmentation Via Transfer Learning Across Diseases -- Generating Virtual Chromoendoscopic Images and Improving Detectability and Classification Performance of Endoscopic Lesions -- MIL3ID 2019 -- Self-supervised learning of inverse problem solvers in medical imaging -- Weakly Supervised Segmentation of Vertebral Bodies with Iterative Slice-propagation -- A Cascade Attention Network for Liver Lesion Classification in Weakly-labeled Multi-phase CT Images -- CT Data Curation for Liver Patients: Phase Recognition in Dynamic Contrast-Enhanced CT -- Active Learning Technique for Multimodal Brain Tumor Segmentation using Limited Labeled Images -- Semi-supervised Learning of Fetal Anatomy from Ultrasound -- Multi-modal segmentation with missing MR sequences using pre-trained fusion networks -- More unlabelled data or label more data? A study on semi-supervised laparoscopic image segmentation -- Few-shot Learning with Deep Triplet Networks for Brain Imaging Modality Recognition -- A Convolutional Neural Network Method for Boundary Optimization Enables Few-Shot Learning for Biomedical Image Segmentation -- Transfer Learning from Partial Annotations for Whole Brain Segmentation -- Learning to Segment Skin Lesions from Noisy Annotations -- A Weakly Supervised Method for Instance Segmentation of Biological Cells -- Towards Practical Unsupervised Anomaly Detection on Retinal Images -- Fine tuning U-Net for ultrasound image segmentation: which layers -- Multi-task Learning for Neonatal Brain Segmentation Using 3D Dense-Unet with Dense Attention Guided by Geodesic Distance. |
| Record Nr. | UNISA-996466435303316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data : First MICCAI Workshop, DART 2019, and First International Workshop, MIL3ID 2019, Shenzhen, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings / / edited by Qian Wang, Fausto Milletari, Hien V. Nguyen, Shadi Albarqouni, M. Jorge Cardoso, Nicola Rieke, Ziyue Xu, Konstantinos Kamnitsas, Vishal Patel, Badri Roysam, Steve Jiang, Kevin Zhou, Khoa Luu, Ngan Le
| Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data : First MICCAI Workshop, DART 2019, and First International Workshop, MIL3ID 2019, Shenzhen, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings / / edited by Qian Wang, Fausto Milletari, Hien V. Nguyen, Shadi Albarqouni, M. Jorge Cardoso, Nicola Rieke, Ziyue Xu, Konstantinos Kamnitsas, Vishal Patel, Badri Roysam, Steve Jiang, Kevin Zhou, Khoa Luu, Ngan Le |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XVII, 254 p. 113 illus., 79 illus. in color.) |
| Disciplina |
616.07540285
616.0754 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Computer vision
Artificial intelligence Medical informatics Computer Vision Artificial Intelligence Health Informatics |
| ISBN | 3-030-33391-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | DART 2019 -- Noise as Domain Shift: Denoising Medical Images by Unpaired Image Translation -- Temporal Consistency Objectives Regularize the Learning of Disentangled Representations -- Multi-layer Domain Adaptation for Deep Convolutional Networks -- Intramodality Domain Adaptation using Self Ensembling and Adversarial Training -- Learning Interpretable Disentangled Representations using Adversarial VAEs -- Synthesising Images and Labels Between MR Sequence Types With CycleGAN -- Multi-Domain Adaptation in Brain MRI through Paired Consistency and Adversarial Learning -- Cross-modality Knowledge Transfer for Prostate Segmentation from CT Scans -- A Pulmonary Nodule Detection Method Based on Residual Learning and Dense Connection -- Harmonization and Targeted Feature Dropout for Generalized Segmentation: Application to Multi-site Traumatic Brain Injury Images -- Improving Pathological Structure Segmentation Via Transfer Learning Across Diseases -- Generating Virtual Chromoendoscopic Imagesand Improving Detectability and Classification Performance of Endoscopic Lesions -- MIL3ID 2019 -- Self-supervised learning of inverse problem solvers in medical imaging -- Weakly Supervised Segmentation of Vertebral Bodies with Iterative Slice-propagation -- A Cascade Attention Network for Liver Lesion Classification in Weakly-labeled Multi-phase CT Images -- CT Data Curation for Liver Patients: Phase Recognition in Dynamic Contrast-Enhanced CT -- Active Learning Technique for Multimodal Brain Tumor Segmentation using Limited Labeled Images -- Semi-supervised Learning of Fetal Anatomy from Ultrasound -- Multi-modal segmentation with missing MR sequences using pre-trained fusion networks -- More unlabelled data or label more data? A study on semi-supervised laparoscopic image segmentation -- Few-shot Learning with Deep Triplet Networks for Brain Imaging Modality Recognition -- A Convolutional Neural Network Method for Boundary Optimization Enables Few-Shot Learning for Biomedical Image Segmentation -- Transfer Learning from Partial Annotations for Whole Brain Segmentation -- Learning to Segment Skin Lesions from Noisy Annotations -- A Weakly Supervised Method for Instance Segmentation of Biological Cells -- Towards Practical Unsupervised Anomaly Detection on Retinal Images -- Fine tuning U-Net for ultrasound image segmentation: which layers -- Multi-task Learning for Neonatal Brain Segmentation Using 3D Dense-Unet with Dense Attention Guided by Geodesic Distance. |
| Record Nr. | UNINA-9910349274803321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Domain adaptation and representation transfer, and affordable healthcare and AI for resource diverse global health : Third MICCAI workshop, DART 2021 and First MICCAI workshop, FAIR 2021 : held in conjunction with MICCAI 2021 : Strasbourg, France, September 27 and October 1, 2021 : proceedings / / edited by Shadi Albarqouni [and nine others]
| Domain adaptation and representation transfer, and affordable healthcare and AI for resource diverse global health : Third MICCAI workshop, DART 2021 and First MICCAI workshop, FAIR 2021 : held in conjunction with MICCAI 2021 : Strasbourg, France, September 27 and October 1, 2021 : proceedings / / edited by Shadi Albarqouni [and nine others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (276 pages) |
| Disciplina | 616.0754 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico | Optical data processing |
| ISBN | 3-030-87722-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface DART 2021 -- Preface FAIR 2021 -- Organization -- Contents -- Domain Adaptation and Representation Transfer -- A Systematic Benchmarking Analysis of Transfer Learning for Medical Image Analysis -- 1 Introduction -- 2 Transfer Learning Setup -- 3 Transfer Learning Benchmarking and Analysis -- 4 Conclusion and Future Work -- References -- Self-supervised Multi-scale Consistency for Weakly Supervised Segmentation Learning -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 4 Experiments -- 4.1 Data -- 4.2 Evaluation Protocol -- 4.3 Results -- 5 Conclusion -- References -- FDA: Feature Decomposition and Aggregation for Robust Airway Segmentation -- 1 Introduction -- 2 Method -- 2.1 Learning Transferable Features -- 2.2 Refinement of Transferable Features -- 2.3 Training Loss Functions -- 3 Experiments and Results -- 4 Conclusion -- References -- Adversarial Continual Learning for Multi-domain Hippocampal Segmentation -- 1 Introduction -- 2 Related Work -- 3 Methods -- 4 Datasets and Experiments -- 5 Result and Discussion -- 6 Conclusion -- References -- Self-supervised Multimodal Generalized Zero Shot Learning for Gleason Grading -- 1 Introduction -- 2 Method -- 2.1 Feature Extraction and Transformation -- 2.2 CVAE Based Feature Generator Using Self Supervision -- 3 Experimental Results -- 3.1 Generalized Zero Shot Learning Results -- 4 Conclusion -- References -- Self-supervised Learning of Inter-label Geometric Relationships for Gleason Grade Segmentation -- 1 Introduction -- 2 Method -- 2.1 Geometry Aware Shape Generation -- 2.2 Sample Diversity from Uncertainty Sampling -- 3 Experimental Results -- 3.1 Dataset Description -- 3.2 Experimental Setup, Baselines and Metrics -- 3.3 Segmentation Results on Gleason Training Data -- 4 Conclusion -- References.
Stop Throwing Away Discriminators! Re-using Adversaries for Test-Time Training -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Re-usable Discriminators: Challenges and Proposed Solutions -- 3.2 Architectures and Training Objectives for s and d -- 3.3 Adversarial Test-Time Training: Adapting w -- 4 Experiments -- 4.1 Results and Discussion -- 5 Conclusion -- References -- Transductive Image Segmentation: Self-training and Effect of Uncertainty Estimation -- 1 Introduction -- 2 Methodology -- 2.1 Transductive Learning via Self-training -- 2.2 Analysing Information Gain and Improving Self-training -- 3 Experimental Evaluation -- 3.1 Data and Model Configuration -- 3.2 Comparing Supervised and Transductive Learning -- 3.3 Comparing Inductive and Transductive Semi-supervised Learning -- 3.4 Blinded Comparison via Manual Refinement of Segmentations -- 4 Conclusion -- References -- Unsupervised Domain Adaptation with Semantic Consistency Across Heterogeneous Modalities for MRI Prostate Lesion Segmentation -- 1 Introduction -- 2 Method -- 2.1 Problem Formulation -- 2.2 Implementation Details -- 3 Datasets -- 4 Results -- 5 Conclusion -- References -- Cohort Bias Adaptation in Aggregated Datasets for Lesion Segmentation -- 1 Introduction -- 2 Related Work -- 3 Methods -- 4 Implementation Details -- 4.1 Network Architecture and Training Parameters -- 4.2 Data Set -- 4.3 Evaluation Metrics -- 5 Experiments and Results -- 5.1 Trial Conditioning -- 5.2 Fine-Tuning to New Cohort Bias -- 5.3 Accounting for Complex Cohort Biases - Missing Small Lesions -- 6 Conclusions -- References -- Exploring Deep Registration Latent Spaces -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Deep Learning-Based Registration Scheme -- 3.2 Decomposition of Latent Space -- 3.3 Implementation and Training Details -- 4 Experiments and Results. 4.1 Qualitative Evaluation -- 5 Discussion and Conclusion -- References -- Learning from Partially Overlapping Labels: Image Segmentation Under Annotation Shift -- 1 Introduction -- 2 Learning from Heterogeneously Labeled Data -- 2.1 Problem Definition and Label-Contradiction Issue -- 2.2 Adaptive Cross Entropy for Learning from Data with Heterogeneous Annotations -- 2.3 Learning from Non-annotated Regions via Mean Teacher -- 3 Experiments -- 3.1 Data and Model Configuration -- 3.2 Results -- 4 Conclusion -- References -- Unsupervised Domain Adaption via Similarity-Based Prototypes for Cross-Modality Segmentation -- 1 Introduction -- 2 Methodology -- 2.1 Motivation -- 2.2 Proposed Framework -- 2.3 Feature Prototypes and Class-Wise Similarity Loss -- 2.4 Contrastive Loss via Feature Dictionaries -- 3 Experiments -- 3.1 Datasets and Details -- 3.2 Results and Analysis -- 4 Conclusion -- References -- Affordable AI and Healthcare -- Classification and Generation of Microscopy Images with Plasmodium Falciparum via Artificial Neural Networks Using Low Cost Settings -- 1 Introduction -- 2 Methods and Materials -- 3 Results -- 4 Discussions -- 5 Conclusions -- References -- Contrast and Resolution Improvement of POCUS Using Self-consistent CycleGAN -- 1 Introduction -- 2 Method -- 3 Experimental Results -- 3.1 Qualitative Evaluation -- 3.2 Quantitative Evaluation -- 3.3 Comparative Evaluation -- 4 Conclusion -- References -- Low-Dose Dynamic CT Perfusion Denoising Without Training Data -- 1 Introduction and Related Work -- 2 Methods -- 2.1 Problem Formulation and Strategy -- 2.2 Self-supervised Low-Dose Sinogram-Space Denoising -- 2.3 Unsupervised CBF Map Denoising Using CTP Information -- 2.4 Low-Dose Simulation -- 2.5 DNN Architectures -- 3 Data and Experiments -- 4 Evaluation, Results, and Discussion -- 5 Conclusion -- References. Recurrent Brain Graph Mapper for Predicting Time-Dependent Brain Graph Evaluation Trajectory -- 1 Introduction -- 2 Proposed Method -- 3 Results and Discussion -- 4 Conclusion -- References -- COVID-Net US: A Tailored, Highly Efficient, Self-attention Deep Convolutional Neural Network Design for Detection of COVID-19 Patient Cases from Point-of-Care Ultrasound Imaging -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 COVIDx-US Dataset -- 3.2 Network Design -- 3.3 Explanation-Driven Performance Validation -- 4 Results and Discussion -- 4.1 Quantitative Analysis -- 4.2 Qualitative Analysis -- 5 Conclusions -- References -- Inter-domain Alignment for Predicting High-Resolution Brain Networks Using Teacher-Student Learning -- 1 Introduction -- 2 Methodology -- 3 Results and Discussion -- 4 Conclusion -- References -- Sickle Cell Disease Severity Prediction from Percoll Gradient Images Using Graph Convolutional Networks -- 1 Introduction -- 2 Methodology -- 2.1 Model -- 2.2 Feature Extraction -- 2.3 Graph Convolution Network -- 2.4 Hemoglobin Density Estimation -- 2.5 Similarity Metric -- 3 Experiments -- 3.1 Dataset -- 3.2 Implementation Details -- 3.3 Results -- 3.4 Ablation Study -- 3.5 Discussion -- 4 Conclusion -- References -- Continual Domain Incremental Learning for Chest X-Ray Classification in Low-Resource Clinical Settings -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Proposed Approach -- 4 Experiments and Results -- 4.1 Results -- 5 Conclusion -- References -- Deep Learning Based Automatic Detection of Adequately Positioned Mammograms -- 1 Introduction -- 2 Data -- 2.1 Data Labeling -- 3 Predicting the PEC and PNL on the MLO View -- 4 Detecting the BB (Nipple) and the PNL on CC View -- 5 Results -- 5.1 Predicting PEC and PNL Lines -- 5.2 Predicting the Adequacy of MLO -- 5.3 Predicting the Positioning of CC View of the Mammogram. 5.4 Predicting the Adequacy of the MLO/CC Pair -- 5.5 Generating an Automated Report on the Positioning of the Breast: Real-World Application -- 6 Discussion and Future Work -- References -- Can Non-specialists Provide High Quality Gold Standard Labels in Challenging Modalities? -- 1 Introduction -- 2 Method -- 3 Experiments and Results -- 4 Discussion -- 5 Conclusion -- References -- Author Index. |
| Record Nr. | UNISA-996464448503316 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health : Third MICCAI Workshop, DART 2021, and First MICCAI Workshop, FAIR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27 and October 1, 2021, Proceedings / / edited by Shadi Albarqouni, M. Jorge Cardoso, Qi Dou, Konstantinos Kamnitsas, Bishesh Khanal, Islem Rekik, Nicola Rieke, Debdoot Sheet, Sotirios Tsaftaris, Daguang Xu, Ziyue Xu
| Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health : Third MICCAI Workshop, DART 2021, and First MICCAI Workshop, FAIR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27 and October 1, 2021, Proceedings / / edited by Shadi Albarqouni, M. Jorge Cardoso, Qi Dou, Konstantinos Kamnitsas, Bishesh Khanal, Islem Rekik, Nicola Rieke, Debdoot Sheet, Sotirios Tsaftaris, Daguang Xu, Ziyue Xu |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (276 pages) |
| Disciplina | 616.0754 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Computer vision
Artificial intelligence Bioinformatics Medical informatics Computer Vision Artificial Intelligence Computational and Systems Biology Health Informatics |
| ISBN | 3-030-87722-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Domain Adaptation and Representation Transfer -- A Systematic Benchmarking Analysis of Transfer Learning for Medical Image Analysis -- Self-supervised Multi-scale Consistency for Weakly Supervised Segmentation Learning -- FDA: Feature Decomposition and Aggregation for Robust Airway Segmentation -- Adversarial Continual Learning for Multi-Domain Hippocampal Segmentation -- Self-Supervised Multimodal Generalized Zero Shot Learning For Gleason Grading -- Self-Supervised Learning of Inter-Label Geometric Relationships For Gleason Grade Segmentation -- Stop Throwing Away Discriminators! Re-using Adversaries for Test-Time Training -- Transductive image segmentation: Self-training and effect of uncertainty estimation -- Unsupervised Domain Adaptation with Semantic Consistency across Heterogeneous Modalities for MRI Prostate Lesion Segmentation -- Cohort Bias Adaptation in Federated Datasets for Lesion Segmentation -- Exploring Deep Registration Latent Spaces -- Learning from Partially Overlapping Labels: Image Segmentation under Annotation Shift -- Unsupervised Domain Adaption via Similarity-based Prototypes for Cross-Modality Segmentation -- A ordable AI and Healthcare -- Classification and Generation of Microscopy Images with Plasmodium Falciparum via Arti cial Neural Networks using Low Cost Settings -- Contrast and Resolution Improvement of POCUS Using Self-Consistent CycleGAN -- Low-Dose Dynamic CT Perfusion Denoising without Training Data -- Recurrent Brain Graph Mapper for Predicting Time-Dependent Brain Graph Evaluation Trajectory -- COVID-Net US: A Tailored, Highly Efficient, Self-Attention Deep Convolutional Neural Network Design for Detection of COVID-19Patient Cases from Point-of-care Ultrasound Imaging -- Inter-Domain Alignment for Predicting High-Resolution Brain Networks Using Teacher-Student Learning -- Sickle Cell Disease Severity Prediction from Percoll Gradient Images using Graph Convolutional Networks -- Continual Domain Incremental Learning for Chest X-ray Classificationin Low-Resource Clinical Settings -- Deep learning based Automatic detection of adequately positioned mammograms -- Can non-specialists provide high quality Gold standard labels in challenging modalities. |
| Record Nr. | UNINA-9910502997403321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Domain adaptation and representation transfer, and distributed and collaborative learning : second MICCAI Workshop, DART 2020, and first MICCAI Workshop, DCL 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020, Proceedings / / Shadi Albarqouni [and ten others], editors
| Domain adaptation and representation transfer, and distributed and collaborative learning : second MICCAI Workshop, DART 2020, and first MICCAI Workshop, DCL 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020, Proceedings / / Shadi Albarqouni [and ten others], editors |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2020] |
| Descrizione fisica | 1 online resource (XIII, 212 p. 86 illus., 67 illus. in color.) |
| Disciplina | 616.07540285 |
| Collana | Lecture notes in computer science |
| Soggetto topico | Diagnostic imaging - Data processing |
| ISBN | 3-030-60548-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | a-Unet++:A Data-driven Neural Network Architecture for Medical Image Segmentation -- DAPR-Net: Domain Adaptive Predicting-refinement Network for Retinal Vessel Segmentation -- Augmented Radiology: Patient-wise Feature Transfer Model for Glioma Grading -- Attention-Guided Deep Domain Adaptation for Brain Dementia Identication with Multi-Site Neuroimaging Data -- Registration of Histopathology Images Using Self Supervised Fine Grained Feature Maps -- Cross-Modality Segmentation by Self-Supervised Semantic Alignment in Disentangled Content Space -- Semi-supervised Pathology Segmentation with Disentangled Representations -- Domain Generalizer: A Few-shot Meta Learning Framework for Domain Generalization in Medical Imaging -- Parts2Whole: Self-supervised Contrastive Learning via Reconstruction -- Cross-View Label Transfer in Knee MR Segmentation Using Iterative Context Learning -- Continual Class Incremental Learning for CT Thoracic Segmentation -- First U-Net Layers Contain More Domain Specific Information Than The Last Ones -- Siloed Federated Learning for Multi-Centric Histopathology Datasets -- On the Fairness of Privacy-Preserving Representations in Medical Applications -- Inverse Distance Aggregation for Federated Learning with Non-IID Data -- Weight Erosion: an Update Aggregation Scheme for Personalized Collaborative Machine Learning -- Federated Gradient Averaging for Multi-Site Training with Momentum-Based Optimizers -- Federated Learning for Breast Density Classification: A Real-World Implementation -- Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning -- Fed-BioMed: A general open-source frontend framework for federated learning in healthcare. |
| Record Nr. | UNISA-996418311903316 |
| Cham, Switzerland : , : Springer, , [2020] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning : Second MICCAI Workshop, DART 2020, and First MICCAI Workshop, DCL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings / / edited by Shadi Albarqouni, Spyridon Bakas, Konstantinos Kamnitsas, M. Jorge Cardoso, Bennett Landman, Wenqi Li, Fausto Milletari, Nicola Rieke, Holger Roth, Daguang Xu, Ziyue Xu
| Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning : Second MICCAI Workshop, DART 2020, and First MICCAI Workshop, DCL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings / / edited by Shadi Albarqouni, Spyridon Bakas, Konstantinos Kamnitsas, M. Jorge Cardoso, Bennett Landman, Wenqi Li, Fausto Milletari, Nicola Rieke, Holger Roth, Daguang Xu, Ziyue Xu |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XIII, 212 p. 86 illus., 67 illus. in color.) |
| Disciplina | 616.07540285 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Computer vision
Social sciences - Data processing Education - Data processing Machine learning Application software Computer Vision Computer Application in Social and Behavioral Sciences Computers and Education Machine Learning Computer and Information Systems Applications |
| ISBN | 3-030-60548-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | a-Unet++:A Data-driven Neural Network Architecture for Medical Image Segmentation -- DAPR-Net: Domain Adaptive Predicting-refinement Network for Retinal Vessel Segmentation -- Augmented Radiology: Patient-wise Feature Transfer Model for Glioma Grading -- Attention-Guided Deep Domain Adaptation for Brain Dementia Identication with Multi-Site Neuroimaging Data -- Registration of Histopathology Images Using Self Supervised Fine Grained Feature Maps -- Cross-Modality Segmentation by Self-Supervised Semantic Alignment in Disentangled Content Space -- Semi-supervised Pathology Segmentation with Disentangled Representations -- Domain Generalizer: A Few-shot Meta Learning Framework for Domain Generalization in Medical Imaging -- Parts2Whole: Self-supervised Contrastive Learning via Reconstruction -- Cross-View Label Transfer in Knee MR Segmentation Using Iterative Context Learning -- Continual Class Incremental Learning for CT Thoracic Segmentation -- First U-Net Layers Contain More Domain SpecificInformation Than The Last Ones -- Siloed Federated Learning for Multi-Centric Histopathology Datasets -- On the Fairness of Privacy-Preserving Representations in Medical Applications -- Inverse Distance Aggregation for Federated Learning with Non-IID Data -- Weight Erosion: an Update Aggregation Scheme for Personalized Collaborative Machine Learning -- Federated Gradient Averaging for Multi-Site Training with Momentum-Based Optimizers -- Federated Learning for Breast Density Classification: A Real-World Implementation -- Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning -- Fed-BioMed: A general open-source frontend framework for federated learning in healthcare. |
| Record Nr. | UNINA-9910427712903321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis [[electronic resource] ] : 7th Joint International Workshop, CVII-STENT 2018 and Third International Workshop, LABELS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings / / edited by Danail Stoyanov, Zeike Taylor, Simone Balocco, Raphael Sznitman, Anne Martel, Lena Maier-Hein, Luc Duong, Guillaume Zahnd, Stefanie Demirci, Shadi Albarqouni, Su-Lin Lee, Stefano Moriconi, Veronika Cheplygina, Diana Mateus, Emanuele Trucco, Eric Granger, Pierre Jannin
| Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis [[electronic resource] ] : 7th Joint International Workshop, CVII-STENT 2018 and Third International Workshop, LABELS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings / / edited by Danail Stoyanov, Zeike Taylor, Simone Balocco, Raphael Sznitman, Anne Martel, Lena Maier-Hein, Luc Duong, Guillaume Zahnd, Stefanie Demirci, Shadi Albarqouni, Su-Lin Lee, Stefano Moriconi, Veronika Cheplygina, Diana Mateus, Emanuele Trucco, Eric Granger, Pierre Jannin |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (xvii, 202 pages) : color illustrations |
| Disciplina | 651.504261 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Optical data processing
Health informatics Artificial intelligence Computer organization Image Processing and Computer Vision Health Informatics Artificial Intelligence Computer Systems Organization and Communication Networks |
| ISBN | 3-030-01364-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Blood-flow estimation in the hepatic arteries based on 3D/2D angiography registration -- Automated quantification of blood flow velocity from time-resolved CT angiography -- Multiple device segmentation for fluoroscopic imaging using multi-task learning -- Segmentation of the Aorta Using Active Contours with Histogram-Based Descriptors -- Layer Separation in X-ray Angiograms for Vessel Enhancement with Fully Convolutional Network -- Generation of a HER2 breast cancer gold-standard using supervised learning from multiple experts -- Deep Learning-based Detection and Segmentation for BVS Struts in IVOCT Images -- Towards Automatic Measurement of Type B Aortic Dissection Parameters -- Prediction of FFR from IVUS Images using Machine Learning -- Deep Learning Retinal Vessel Segmentation From a Single Annotated Example: An Application of Cyclic Generative Adversarial Neural Networks -- An Efficient and Comprehensive Labeling Tool for Large-scale Annotation of Fundus Images -- Crowd disagreement about medical images is informative -- Imperfect Segmentation Labels: How Much Do They Matter? -- Crowdsourcing annotation of surgical instruments in videos of cataract surgery -- Four-dimensional ASL MR angiography phantoms with noise learned by neural styling -- Feature learning based on visual similarity triplets in medical image analysis: A case study of emphysema in chest CT scans -- Capsule Networks against Medical Imaging Data Challenges -- Fully Automatic Segmentation of Coronary Arteries based on Deep Neural Network in Intravascular Ultrasound Images -- Weakly-Supervised Learning for Tool Localization in Laparoscopic Videos -- Radiology Objects in COntext (ROCO) -- Improving out-of-sample prediction of quality of MRIQC. |
| Record Nr. | UNISA-996466467603316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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