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Applications of Medical Artificial Intelligence [[electronic resource] ] : Second International Workshop, AMAI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings / / edited by Shandong Wu, Behrouz Shabestari, Lei Xing
| Applications of Medical Artificial Intelligence [[electronic resource] ] : Second International Workshop, AMAI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings / / edited by Shandong Wu, Behrouz Shabestari, Lei Xing |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (187 pages) |
| Disciplina | 610.28563 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Computer vision
Application software Artificial intelligence Education - Data processing Social sciences - Data processing Computer Vision Computer and Information Systems Applications Artificial Intelligence Computers and Education Computer Application in Social and Behavioral Sciences |
| ISBN | 3-031-47076-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Clinical Trial Histology Image Based End-to-End Biomarker Expression Levels Prediction and Visualization Using Constrained GANs -- 1 Introduction -- 2 Clinical Trial Data -- 3 Method -- 3.1 Whole Slide Image Registration -- 3.2 Image Tiling and Pre-processing -- 3.3 Activate Learning (AL) for Semantic Segmentation -- 3.4 Biomarker Expression Proportion Score (PS) -- 3.5 Constrained GAN for Virtual IHC Synthesis -- 4 Results and Discussion -- 4.1 Phase 1 Clinical Trial Dataset -- 4.2 Active Learning and PS Score Prediction -- 4.3 Constrained GAN Training and Testing -- 5 Conclusion and Future Work -- References -- More Than Meets the Eye: Physicians' Visual Attention in the Operating Room -- 1 Introduction -- 2 Related Work -- 2.1 Face Detection -- 2.2 Facial Landmarks -- 2.3 Spatiotemporal Gaze Architecture -- 2.4 Eye-Context Interaction Inferring Network -- 3 Materials -- 3.1 Benchmark Datasets -- 3.2 Our Datasets -- 4 Methods -- 4.1 Pipeline Construction -- 4.2 Implementation -- 5 Results -- 5.1 Ablation Study of the End-to-End Pipeline -- 5.2 Evaluation of the Proposed Framework on Our Datasets -- 6 Discussion -- References -- CNNs vs. Transformers: Performance and Robustness in Endoscopic Image Analysis -- 1 Introduction -- 2 Methods -- 2.1 Data: Setting, Datasets and Preprocessing -- 2.2 Network Architectures, Training and Evaluation -- 3 Experimental Results and Discussion -- 4 Conclusions -- References -- Investigating the Impact of Image Quality on Endoscopic AI Model Performance -- 1 Introduction -- 2 Methods and Materials -- 2.1 Network Training -- 2.2 Experiments -- 2.3 Image Corruptions -- 2.4 Evaluation -- 3 Results -- 4 Conclusions and Future Work -- References -- Ensembling Voxel-Based and Box-Based Model Predictions for Robust Lesion Detection -- 1 Introduction.
2 Materials and Methods -- 2.1 Imaging Data -- 2.2 Ensembling Method -- 2.3 Evaluation -- 3 Results -- 4 Discussion and Conclusion -- References -- Advancing Abdominal Organ and PDAC Segmentation Accuracy with Task-Specific Interactive Models -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Dataset -- 3.2 Interaction Framework -- 3.3 Training Details and Evaluation Criteria -- 4 Results -- 5 Discussion -- 6 Future Work -- 7 Conclusion -- References -- Anatomical Location-Guided Deep Learning-Based Genetic Cluster Identification of Pheochromocytomas and Paragangliomas from CT Images -- 1 Introduction -- 2 Methods -- 2.1 Dataset -- 2.2 PPGL-Transformer -- 2.3 Supervised Contrastive Learning of PPGL-Transformer -- 2.4 Implementation and Evaluation Details -- 3 Results -- 4 Discussion and Conclusion -- References -- Video-Based Gait Analysis for Assessing Alzheimer's Disease and Dementia with Lewy Bodies -- 1 Introduction and Related Work -- 2 Method -- 2.1 Our Patient Data -- 2.2 MAX-GRNet for 3D Pose Estimation -- 2.3 Geometric Deep Learning for Severity Assessment -- 3 Experiments -- 3.1 Datasets -- 3.2 Implementation Details -- 3.3 Results -- 4 Conclusion -- References -- Enhancing Clinical Support for Breast Cancer with Deep Learning Models Using Synthetic Correlated Diffusion Imaging -- 1 Introduction -- 2 Related Works -- 2.1 Breast Cancer Grading -- 2.2 Pathologic Complete Response Prediction -- 3 Methodology -- 3.1 Patient Cohort and Imaging Protocol -- 3.2 Extracting Deep Radiomic Sequences from CDIs -- 3.3 pCR Prediction via Volumetric Deep Radiomic Features -- 4 Results -- 4.1 Breast Cancer Grading (SBR Grade) -- 4.2 pCR Prediction via Volumetric Deep Radiomic Features -- 5 Conclusion -- References -- Image-Based 3D Reconstruction of Cleft Lip and Palate Using a Learned Shape Prior -- 1 Introduction -- 2 Related Work -- 3 Methods. 3.1 Data Pre-processing -- 3.2 Semi-dense Reconstruction -- 3.3 Data-Driven Shape Prior -- 3.4 Data Collection -- 4 Results -- 4.1 3D Reconstruction -- 4.2 Plate Evaluation -- 4.3 Learned Shape Prior -- 5 Conclusion -- References -- Breaking down the Hierarchy: A New Approach to Leukemia Classification -- 1 Introduction -- 2 Related Work -- 3 Dataset -- 3.1 Pre-processing -- 4 Methodology Overview -- 4.1 Flat/Leaf Classification (Baseline) -- 4.2 Hierarchical Multi-label Classification -- 5 Experimental Setup -- 5.1 Model Selection -- 5.2 Simulating Pathological Evaluation -- 5.3 Experimental Procedures -- 5.4 Evaluation Metrics -- 6 Results and Discussion -- 6.1 Flat/Leaf Classification -- 6.2 Base vs Proposed Hierarchical Classification -- 6.3 Flat vs Proposed Hierarchical Classification -- 6.4 Visual Experimental Results -- 7 Conclusion -- References -- Single-Cell Spatial Analysis of Histopathology Images for Survival Prediction via Graph Attention Network -- 1 Introduction -- 2 Method -- 2.1 Nuclei Classification -- 2.2 Cell Graph Construction -- 2.3 Graph Attention Network -- 2.4 Model Interpretability -- 3 Experimental and Results -- 3.1 Dataset -- 3.2 Implementation Details -- 3.3 Comparison with State-of-the-Art Methods -- 4 Conclusion -- References -- Ultrafast Labeling for Multiplexed Immunobiomarkers from Label-free Fluorescent Images -- 1 Introduction -- 2 Method -- 2.1 Framework Overview -- 2.2 Random MAE Feature Extractor -- 2.3 Dual-Modal Feature Combination With Self-Attention Mechanism -- 2.4 Multitasked Composite Loss Function -- 3 Experiments -- 3.1 Data Preparation -- 3.2 Assessment Scores -- 3.3 Software and Platform -- 4 Results -- 4.1 Comparison with Other Techniques -- 4.2 Clinical Observation Assessment -- 5 Conclusion -- References. M U-Net: Intestine Segmentation Using Multi-dimensional Features for Ileus Diagnosis Assistance -- 1 Introduction -- 2 Method -- 2.1 Overview -- 2.2 Compound Loss Function with Deep Supervision -- 3 Experiments and Results -- 3.1 Experiment Detail -- 3.2 Results -- 4 Discussion and Conclusions -- References -- Enhancing Cardiac MRI Segmentation via Classifier-Guided Two-Stage Network and All-Slice Information Fusion Transformer -- 1 Introduction -- 2 Methods -- 2.1 Two-Stage Based Segmentation -- 2.2 All-Slice Fusion Transformer -- 2.3 Classifier-Guided Segmentation Refinement -- 2.4 Loss Functions -- 3 Experiments -- 3.1 Datasets and Settings -- 3.2 Results -- 3.3 Ablation Study -- 4 Discussion and Conclusion -- References -- Accessible Otitis Media Screening with a Deep Learning-Powered Mobile Otoscope -- 1 Introduction -- 2 Methodology -- 2.1 Mobile Otoscope -- 2.2 Deep Learning -- 2.3 Smartphone Application -- 3 Results -- 3.1 Algorithmic Testing -- 3.2 Validation Testing -- 4 Discussion and Conclusions -- 4.1 Prospect of Application -- References -- Feature Selection for Malapposition Detection in Intravascular Ultrasound - A Comparative Study -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Masking Irrelevant Regions -- 3.2 Including Temporal Context -- 3.3 Including Blood Flow Information via Deformation Fields -- 3.4 Combining the Modules -- 4 Experiments and Results -- 4.1 Training Modules Needed for Defining M and V -- 4.2 Single-Frame Classification -- 4.3 Classification Using Temporal Context -- 5 Conclusion -- References -- Author Index. |
| Record Nr. | UNISA-996558465703316 |
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Applications of Medical Artificial Intelligence : Second International Workshop, AMAI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings / / edited by Shandong Wu, Behrouz Shabestari, Lei Xing
| Applications of Medical Artificial Intelligence : Second International Workshop, AMAI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings / / edited by Shandong Wu, Behrouz Shabestari, Lei Xing |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (187 pages) |
| Disciplina | 610.28563 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Computer vision
Application software Artificial intelligence Education - Data processing Social sciences - Data processing Computer Vision Computer and Information Systems Applications Artificial Intelligence Computers and Education Computer Application in Social and Behavioral Sciences |
| ISBN | 3-031-47076-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Clinical Trial Histology Image Based End-to-End Biomarker Expression Levels Prediction and Visualization Using Constrained GANs -- 1 Introduction -- 2 Clinical Trial Data -- 3 Method -- 3.1 Whole Slide Image Registration -- 3.2 Image Tiling and Pre-processing -- 3.3 Activate Learning (AL) for Semantic Segmentation -- 3.4 Biomarker Expression Proportion Score (PS) -- 3.5 Constrained GAN for Virtual IHC Synthesis -- 4 Results and Discussion -- 4.1 Phase 1 Clinical Trial Dataset -- 4.2 Active Learning and PS Score Prediction -- 4.3 Constrained GAN Training and Testing -- 5 Conclusion and Future Work -- References -- More Than Meets the Eye: Physicians' Visual Attention in the Operating Room -- 1 Introduction -- 2 Related Work -- 2.1 Face Detection -- 2.2 Facial Landmarks -- 2.3 Spatiotemporal Gaze Architecture -- 2.4 Eye-Context Interaction Inferring Network -- 3 Materials -- 3.1 Benchmark Datasets -- 3.2 Our Datasets -- 4 Methods -- 4.1 Pipeline Construction -- 4.2 Implementation -- 5 Results -- 5.1 Ablation Study of the End-to-End Pipeline -- 5.2 Evaluation of the Proposed Framework on Our Datasets -- 6 Discussion -- References -- CNNs vs. Transformers: Performance and Robustness in Endoscopic Image Analysis -- 1 Introduction -- 2 Methods -- 2.1 Data: Setting, Datasets and Preprocessing -- 2.2 Network Architectures, Training and Evaluation -- 3 Experimental Results and Discussion -- 4 Conclusions -- References -- Investigating the Impact of Image Quality on Endoscopic AI Model Performance -- 1 Introduction -- 2 Methods and Materials -- 2.1 Network Training -- 2.2 Experiments -- 2.3 Image Corruptions -- 2.4 Evaluation -- 3 Results -- 4 Conclusions and Future Work -- References -- Ensembling Voxel-Based and Box-Based Model Predictions for Robust Lesion Detection -- 1 Introduction.
2 Materials and Methods -- 2.1 Imaging Data -- 2.2 Ensembling Method -- 2.3 Evaluation -- 3 Results -- 4 Discussion and Conclusion -- References -- Advancing Abdominal Organ and PDAC Segmentation Accuracy with Task-Specific Interactive Models -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Dataset -- 3.2 Interaction Framework -- 3.3 Training Details and Evaluation Criteria -- 4 Results -- 5 Discussion -- 6 Future Work -- 7 Conclusion -- References -- Anatomical Location-Guided Deep Learning-Based Genetic Cluster Identification of Pheochromocytomas and Paragangliomas from CT Images -- 1 Introduction -- 2 Methods -- 2.1 Dataset -- 2.2 PPGL-Transformer -- 2.3 Supervised Contrastive Learning of PPGL-Transformer -- 2.4 Implementation and Evaluation Details -- 3 Results -- 4 Discussion and Conclusion -- References -- Video-Based Gait Analysis for Assessing Alzheimer's Disease and Dementia with Lewy Bodies -- 1 Introduction and Related Work -- 2 Method -- 2.1 Our Patient Data -- 2.2 MAX-GRNet for 3D Pose Estimation -- 2.3 Geometric Deep Learning for Severity Assessment -- 3 Experiments -- 3.1 Datasets -- 3.2 Implementation Details -- 3.3 Results -- 4 Conclusion -- References -- Enhancing Clinical Support for Breast Cancer with Deep Learning Models Using Synthetic Correlated Diffusion Imaging -- 1 Introduction -- 2 Related Works -- 2.1 Breast Cancer Grading -- 2.2 Pathologic Complete Response Prediction -- 3 Methodology -- 3.1 Patient Cohort and Imaging Protocol -- 3.2 Extracting Deep Radiomic Sequences from CDIs -- 3.3 pCR Prediction via Volumetric Deep Radiomic Features -- 4 Results -- 4.1 Breast Cancer Grading (SBR Grade) -- 4.2 pCR Prediction via Volumetric Deep Radiomic Features -- 5 Conclusion -- References -- Image-Based 3D Reconstruction of Cleft Lip and Palate Using a Learned Shape Prior -- 1 Introduction -- 2 Related Work -- 3 Methods. 3.1 Data Pre-processing -- 3.2 Semi-dense Reconstruction -- 3.3 Data-Driven Shape Prior -- 3.4 Data Collection -- 4 Results -- 4.1 3D Reconstruction -- 4.2 Plate Evaluation -- 4.3 Learned Shape Prior -- 5 Conclusion -- References -- Breaking down the Hierarchy: A New Approach to Leukemia Classification -- 1 Introduction -- 2 Related Work -- 3 Dataset -- 3.1 Pre-processing -- 4 Methodology Overview -- 4.1 Flat/Leaf Classification (Baseline) -- 4.2 Hierarchical Multi-label Classification -- 5 Experimental Setup -- 5.1 Model Selection -- 5.2 Simulating Pathological Evaluation -- 5.3 Experimental Procedures -- 5.4 Evaluation Metrics -- 6 Results and Discussion -- 6.1 Flat/Leaf Classification -- 6.2 Base vs Proposed Hierarchical Classification -- 6.3 Flat vs Proposed Hierarchical Classification -- 6.4 Visual Experimental Results -- 7 Conclusion -- References -- Single-Cell Spatial Analysis of Histopathology Images for Survival Prediction via Graph Attention Network -- 1 Introduction -- 2 Method -- 2.1 Nuclei Classification -- 2.2 Cell Graph Construction -- 2.3 Graph Attention Network -- 2.4 Model Interpretability -- 3 Experimental and Results -- 3.1 Dataset -- 3.2 Implementation Details -- 3.3 Comparison with State-of-the-Art Methods -- 4 Conclusion -- References -- Ultrafast Labeling for Multiplexed Immunobiomarkers from Label-free Fluorescent Images -- 1 Introduction -- 2 Method -- 2.1 Framework Overview -- 2.2 Random MAE Feature Extractor -- 2.3 Dual-Modal Feature Combination With Self-Attention Mechanism -- 2.4 Multitasked Composite Loss Function -- 3 Experiments -- 3.1 Data Preparation -- 3.2 Assessment Scores -- 3.3 Software and Platform -- 4 Results -- 4.1 Comparison with Other Techniques -- 4.2 Clinical Observation Assessment -- 5 Conclusion -- References. M U-Net: Intestine Segmentation Using Multi-dimensional Features for Ileus Diagnosis Assistance -- 1 Introduction -- 2 Method -- 2.1 Overview -- 2.2 Compound Loss Function with Deep Supervision -- 3 Experiments and Results -- 3.1 Experiment Detail -- 3.2 Results -- 4 Discussion and Conclusions -- References -- Enhancing Cardiac MRI Segmentation via Classifier-Guided Two-Stage Network and All-Slice Information Fusion Transformer -- 1 Introduction -- 2 Methods -- 2.1 Two-Stage Based Segmentation -- 2.2 All-Slice Fusion Transformer -- 2.3 Classifier-Guided Segmentation Refinement -- 2.4 Loss Functions -- 3 Experiments -- 3.1 Datasets and Settings -- 3.2 Results -- 3.3 Ablation Study -- 4 Discussion and Conclusion -- References -- Accessible Otitis Media Screening with a Deep Learning-Powered Mobile Otoscope -- 1 Introduction -- 2 Methodology -- 2.1 Mobile Otoscope -- 2.2 Deep Learning -- 2.3 Smartphone Application -- 3 Results -- 3.1 Algorithmic Testing -- 3.2 Validation Testing -- 4 Discussion and Conclusions -- 4.1 Prospect of Application -- References -- Feature Selection for Malapposition Detection in Intravascular Ultrasound - A Comparative Study -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Masking Irrelevant Regions -- 3.2 Including Temporal Context -- 3.3 Including Blood Flow Information via Deformation Fields -- 3.4 Combining the Modules -- 4 Experiments and Results -- 4.1 Training Modules Needed for Defining M and V -- 4.2 Single-Frame Classification -- 4.3 Classification Using Temporal Context -- 5 Conclusion -- References -- Author Index. |
| Record Nr. | UNINA-9910760260303321 |
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Applications of medical artificial intelligence : first international workshop, AMAI 2022, held in conjunction with MICCAI 2022, Singapore, September 18, 2022, proceedings / / edited by Shandong Wu, Behrouz Shabestari, and Lei Xing
| Applications of medical artificial intelligence : first international workshop, AMAI 2022, held in conjunction with MICCAI 2022, Singapore, September 18, 2022, proceedings / / edited by Shandong Wu, Behrouz Shabestari, and Lei Xing |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (171 pages) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Computer Science Ser. |
| Soggetto topico |
Artificial intelligence - Medical applications
Diagnostic imaging - Data processing |
| ISBN | 3-031-17721-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Increasing the Accessibility of Peripheral Artery Disease Screening with Deep Learning -- 1 Problem -- 2 Related Work -- 3 Data Collection Study -- 4 System Development -- 5 Validation Study -- 6 Conclusion -- References -- Deep Learning Meets Computational Fluid Dynamics to Assess CAD in CCTA -- 1 Introduction -- 2 Automated Assessment of CAD in CCTA -- 2.1 Straightened Representation of the Coronary Vessels -- 2.2 Representing Ground-Truth Segmentation as a 3D Mesh -- 2.3 Segmentation of Vessels Using U-Nets in Upsampled CTTA -- 2.4 Blood Flow Simulation -- 3 Experimental Validation -- 4 Conclusions and Future Work -- References -- Machine Learning for Dynamically Predicting the Onset of Renal Replacement Therapy in Chronic Kidney Disease Patients Using Claims Data -- 1 Introduction -- 2 Methods -- 2.1 Dataset Description -- 2.2 Task Definition -- 2.3 Data Representation and Processing -- 2.4 Model Description -- 2.5 Model Evaluation -- 3 Experiments and Results -- 3.1 Study Population and Dataset -- 3.2 Model Performance -- 4 Conclusions -- References -- Uncertainty-Aware Geographic Atrophy Progression Prediction from Fundus Autofluorescence -- 1 Introduction -- 2 Method -- 2.1 Data -- 2.2 Model Development -- 2.3 Uncertainty Estimation Using Deep Ensemble -- 3 Results -- 4 Conclusions -- References -- Automated Assessment of Renal Calculi in Serial Computed Tomography Scans -- 1 Introduction -- 1.1 Our Contributions -- 2 Materials and Methods -- 2.1 Data -- 2.2 Calculi Detection and Segmentation -- 2.3 Registration and Stone Matching -- 2.4 Manual Review and Tracking -- 2.5 Evaluation of Performance -- 2.6 Statistical Analysis -- 3 Results -- 3.1 Cohort Characteristics -- 3.2 Performance of the Stone Detection and Segmentation -- 3.3 Performance of Stone Tracking -- 4 Discussion -- References.
Prediction of Mandibular ORN Incidence from 3D Radiation Dose Distribution Maps Using Deep Learning -- 1 Introduction -- 2 Methods and Materials -- 2.1 Data -- 2.2 Prediction Models -- 2.3 Model Evaluation -- 2.4 Statistical Analysis -- 3 Results -- 4 Discussion -- 4.1 ORN Prediction -- 4.2 Study Limitations and Future Work -- 5 Conclusion -- References -- Analysis of Potential Biases on Mammography Datasets for Deep Learning Model Development -- 1 Introduction -- 2 Materials and Methods -- 2.1 Mammography Dataset -- 2.2 Bias Analysis -- 2.3 Bias Correction Techniques -- 2.4 Experimental Setup -- 3 Results and Discussion -- 4 Conclusions -- References -- ECG-ATK-GAN: Robustness Against Adversarial Attacks on ECGs Using Conditional Generative Adversarial Networks -- 1 Introduction -- 2 Methodology -- 2.1 Generator and Discriminator -- 2.2 Objective Function and Individual Losses -- 2.3 Adversarial Attacks -- 3 Experiments -- 3.1 Data Set Preparation -- 3.2 Hyper-parameters -- 3.3 Quantitative Evaluation -- 3.4 Qualitative Evaluation -- 4 Conclusions and Future Work -- References -- CADIA: A Success Story in Breast Cancer Diagnosis with Digital Pathology and AI Image Analysis -- 1 Introduction -- 2 Methods -- 2.1 Starting Point Analysis and Functional Requirement Collection -- 2.2 Sample Selection and Collection -- 2.3 Digital Image Annotation -- 2.4 Model Development -- 2.5 Model Deployment and Integration -- 3 Results -- 4 Conclusions and Future Perspectives -- References -- Was that so Hard? Estimating Human Classification Difficulty -- 1 Introduction -- 2 Estimating Image Difficulty -- 3 Datasets -- 4 Experiments -- 5 Results -- 6 Discussion and Conclusion -- References -- A Deep Learning-Based Interactive Medical Image Segmentation Framework -- 1 Introduction -- 2 Related Work -- 3 Applicative Scope -- 4 Methodology -- 4.1 System. 4.2 Training with Dynamic Data Generation -- 5 Experimental Results -- 5.1 Setup -- 5.2 Automated Evaluation -- 5.3 User Evaluation -- 6 Conclusion -- References -- Deep Neural Network Pruning for Nuclei Instance Segmentation in Hematoxylin and Eosin-Stained Histological Images -- 1 Introduction -- 2 Method -- 2.1 Datasets -- 2.2 Segmentation and Regression Models -- 2.3 Pruning -- 2.4 Merging and Post-processing -- 2.5 Evaluation Metrics -- 3 Results and Discussion -- 4 Conclusion -- References -- Spatial Feature Conservation Networks (SFCNs) for Dilated Convolutions to Improve Breast Cancer Segmentation from DCE-MRI -- 1 Introduction -- 2 Methods -- 2.1 Compensation Module -- 2.2 Network Architecture -- 2.3 Performance Evaluation -- 2.4 Image Dataset and Data Preparation -- 3 Results -- 4 Discussion and Conclusion -- References -- The Impact of Using Voxel-Level Segmentation Metrics on Evaluating Multifocal Prostate Cancer Localisation -- 1 Introduction -- 2 Materials and Methods -- 2.1 Prostate Lesion Segmentation for Procedure Planning -- 2.2 Voxel-Level Segmentation Metrics -- 2.3 Lesion-Level Object Detection Metrics -- 2.4 Lesion Detection Metrics for Multifocal Segmentation Output -- 2.5 Correlation, Pairwise Agreement and Impact on Evaluation -- 3 Results -- 3.1 Comparison Between DSC and HD -- 3.2 Comparison Between Voxel- and Lesion-Level Metrics -- 4 Conclusion -- References -- OOOE: Only-One-Object-Exists Assumption to Find Very Small Objects in Chest Radiographs -- 1 Introduction -- 2 Methods -- 2.1 Feature Extractor -- 2.2 Point Detection Head -- 3 Experiments -- 3.1 Datasets -- 3.2 Evaluation Metrics -- 3.3 Implementation Details -- 3.4 Comparison to Other Methods -- 3.5 A Closer Look at ET-tube vs. T-tube Detection Performance -- 4 Conclusion -- References -- Wavelet Guided 3D Deep Model to Improve Dental Microfracture Detection. 1 Introduction -- 2 Materials -- 3 Methods -- 4 Results and Discussion -- References -- Author Index. |
| Record Nr. | UNISA-996490357403316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Applications of Medical Artificial Intelligence : First International Workshop, AMAI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings / / edited by Shandong Wu, Behrouz Shabestari, Lei Xing
| Applications of Medical Artificial Intelligence : First International Workshop, AMAI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings / / edited by Shandong Wu, Behrouz Shabestari, Lei Xing |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (171 pages) |
| Disciplina |
006.3
616.0754 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Computer vision
Application software Artificial intelligence Education - Data processing Social sciences - Data processing Computer Vision Computer and Information Systems Applications Artificial Intelligence Computers and Education Computer Application in Social and Behavioral Sciences |
| ISBN | 3-031-17721-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Increasing the Accessibility of Peripheral Artery Disease Screening with Deep Learning -- 1 Problem -- 2 Related Work -- 3 Data Collection Study -- 4 System Development -- 5 Validation Study -- 6 Conclusion -- References -- Deep Learning Meets Computational Fluid Dynamics to Assess CAD in CCTA -- 1 Introduction -- 2 Automated Assessment of CAD in CCTA -- 2.1 Straightened Representation of the Coronary Vessels -- 2.2 Representing Ground-Truth Segmentation as a 3D Mesh -- 2.3 Segmentation of Vessels Using U-Nets in Upsampled CTTA -- 2.4 Blood Flow Simulation -- 3 Experimental Validation -- 4 Conclusions and Future Work -- References -- Machine Learning for Dynamically Predicting the Onset of Renal Replacement Therapy in Chronic Kidney Disease Patients Using Claims Data -- 1 Introduction -- 2 Methods -- 2.1 Dataset Description -- 2.2 Task Definition -- 2.3 Data Representation and Processing -- 2.4 Model Description -- 2.5 Model Evaluation -- 3 Experiments and Results -- 3.1 Study Population and Dataset -- 3.2 Model Performance -- 4 Conclusions -- References -- Uncertainty-Aware Geographic Atrophy Progression Prediction from Fundus Autofluorescence -- 1 Introduction -- 2 Method -- 2.1 Data -- 2.2 Model Development -- 2.3 Uncertainty Estimation Using Deep Ensemble -- 3 Results -- 4 Conclusions -- References -- Automated Assessment of Renal Calculi in Serial Computed Tomography Scans -- 1 Introduction -- 1.1 Our Contributions -- 2 Materials and Methods -- 2.1 Data -- 2.2 Calculi Detection and Segmentation -- 2.3 Registration and Stone Matching -- 2.4 Manual Review and Tracking -- 2.5 Evaluation of Performance -- 2.6 Statistical Analysis -- 3 Results -- 3.1 Cohort Characteristics -- 3.2 Performance of the Stone Detection and Segmentation -- 3.3 Performance of Stone Tracking -- 4 Discussion -- References.
Prediction of Mandibular ORN Incidence from 3D Radiation Dose Distribution Maps Using Deep Learning -- 1 Introduction -- 2 Methods and Materials -- 2.1 Data -- 2.2 Prediction Models -- 2.3 Model Evaluation -- 2.4 Statistical Analysis -- 3 Results -- 4 Discussion -- 4.1 ORN Prediction -- 4.2 Study Limitations and Future Work -- 5 Conclusion -- References -- Analysis of Potential Biases on Mammography Datasets for Deep Learning Model Development -- 1 Introduction -- 2 Materials and Methods -- 2.1 Mammography Dataset -- 2.2 Bias Analysis -- 2.3 Bias Correction Techniques -- 2.4 Experimental Setup -- 3 Results and Discussion -- 4 Conclusions -- References -- ECG-ATK-GAN: Robustness Against Adversarial Attacks on ECGs Using Conditional Generative Adversarial Networks -- 1 Introduction -- 2 Methodology -- 2.1 Generator and Discriminator -- 2.2 Objective Function and Individual Losses -- 2.3 Adversarial Attacks -- 3 Experiments -- 3.1 Data Set Preparation -- 3.2 Hyper-parameters -- 3.3 Quantitative Evaluation -- 3.4 Qualitative Evaluation -- 4 Conclusions and Future Work -- References -- CADIA: A Success Story in Breast Cancer Diagnosis with Digital Pathology and AI Image Analysis -- 1 Introduction -- 2 Methods -- 2.1 Starting Point Analysis and Functional Requirement Collection -- 2.2 Sample Selection and Collection -- 2.3 Digital Image Annotation -- 2.4 Model Development -- 2.5 Model Deployment and Integration -- 3 Results -- 4 Conclusions and Future Perspectives -- References -- Was that so Hard? Estimating Human Classification Difficulty -- 1 Introduction -- 2 Estimating Image Difficulty -- 3 Datasets -- 4 Experiments -- 5 Results -- 6 Discussion and Conclusion -- References -- A Deep Learning-Based Interactive Medical Image Segmentation Framework -- 1 Introduction -- 2 Related Work -- 3 Applicative Scope -- 4 Methodology -- 4.1 System. 4.2 Training with Dynamic Data Generation -- 5 Experimental Results -- 5.1 Setup -- 5.2 Automated Evaluation -- 5.3 User Evaluation -- 6 Conclusion -- References -- Deep Neural Network Pruning for Nuclei Instance Segmentation in Hematoxylin and Eosin-Stained Histological Images -- 1 Introduction -- 2 Method -- 2.1 Datasets -- 2.2 Segmentation and Regression Models -- 2.3 Pruning -- 2.4 Merging and Post-processing -- 2.5 Evaluation Metrics -- 3 Results and Discussion -- 4 Conclusion -- References -- Spatial Feature Conservation Networks (SFCNs) for Dilated Convolutions to Improve Breast Cancer Segmentation from DCE-MRI -- 1 Introduction -- 2 Methods -- 2.1 Compensation Module -- 2.2 Network Architecture -- 2.3 Performance Evaluation -- 2.4 Image Dataset and Data Preparation -- 3 Results -- 4 Discussion and Conclusion -- References -- The Impact of Using Voxel-Level Segmentation Metrics on Evaluating Multifocal Prostate Cancer Localisation -- 1 Introduction -- 2 Materials and Methods -- 2.1 Prostate Lesion Segmentation for Procedure Planning -- 2.2 Voxel-Level Segmentation Metrics -- 2.3 Lesion-Level Object Detection Metrics -- 2.4 Lesion Detection Metrics for Multifocal Segmentation Output -- 2.5 Correlation, Pairwise Agreement and Impact on Evaluation -- 3 Results -- 3.1 Comparison Between DSC and HD -- 3.2 Comparison Between Voxel- and Lesion-Level Metrics -- 4 Conclusion -- References -- OOOE: Only-One-Object-Exists Assumption to Find Very Small Objects in Chest Radiographs -- 1 Introduction -- 2 Methods -- 2.1 Feature Extractor -- 2.2 Point Detection Head -- 3 Experiments -- 3.1 Datasets -- 3.2 Evaluation Metrics -- 3.3 Implementation Details -- 3.4 Comparison to Other Methods -- 3.5 A Closer Look at ET-tube vs. T-tube Detection Performance -- 4 Conclusion -- References -- Wavelet Guided 3D Deep Model to Improve Dental Microfracture Detection. 1 Introduction -- 2 Materials -- 3 Methods -- 4 Results and Discussion -- References -- Author Index. |
| Record Nr. | UNINA-9910616210103321 |
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial Intelligence in Radiation Therapy [[electronic resource] ] : First International Workshop, AIRT 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings / / edited by Dan Nguyen, Lei Xing, Steve Jiang
| Artificial Intelligence in Radiation Therapy [[electronic resource] ] : First International Workshop, AIRT 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings / / edited by Dan Nguyen, Lei Xing, Steve Jiang |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XI, 172 p. 87 illus., 74 illus. in color.) |
| Disciplina | 610.28563 |
| 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 | 3-030-32486-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Using Supervised Learning and Guided Monte Carlo Tree Search for Beam Orientation Optimization in Radiation Therapy -- Feasibility of CT-only 3D dose prediction for VMAT prostate plans using deep learning -- Automatically Tracking and Detecting Significant Nodal Mass Shrinkage During Head-and-Neck Radiation Treatment Using Image Saliency -- 4D-CT Deformable Image Registration Using an Unsupervised Deep Convolutional Neural Network -- Toward markerless image-guided radiotherapy using deep learning for prostate cancer -- A Two-Stage Approach for Automated Prostate Lesion Detection and Classification with Mask R-CNN and Weakly Supervised Deep Neural Network -- A Novel Deep Learning Framework for Standardizing the Label of OARs in CT -- Multimodal Volume-Aware Detection and Segmentation for Brain Metastases Radiosurgery -- Voxel-level Radiotherapy Dose Prediction Using Densely Connected Network with Dilated Convolutions -- Online Target Volume Estimation and Prediction From an Interlaced Slice Acquisition - A Manifold Embedding and Learning Approach -- One-dimensional convolutional network for Dosimetry Evaluation at Organs-at-Risk in Esophageal Radiation Treatment Planning -- Unpaired Synthetic Image Generation in Radiology Using GANs -- Deriving lung perfusion directly from CT image using deep convolutional neural network: A preliminary study -- Individualized 3D Dose Distribution Prediction Using Deep Learning -- Deep Generative Model-Driven Multimodal Prostate Segmentation in Radiotherapy -- Dose Distribution Prediction for Optimal Treatment of Modern External Beam Radiation Therapy for Nasopharyngeal Carcinoma -- DeepMCDose: A Deep Learning Method for Efficient Monte Carlo Beamlet Dose Calculation by Predictive Denoising in MR-Guided Radiotherapy -- UC-GAN for MR to CT Image Synthesis -- CBCT-based Synthetic MRI Generation for CBCT-guided Adaptive Radiotherapy -- Cardio-pulmonary Substructure Segmentation of CT images using Convolutional Neural Networks. |
| Record Nr. | UNISA-996466425303316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Artificial Intelligence in Radiation Therapy : First International Workshop, AIRT 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings / / edited by Dan Nguyen, Lei Xing, Steve Jiang
| Artificial Intelligence in Radiation Therapy : First International Workshop, AIRT 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings / / edited by Dan Nguyen, Lei Xing, Steve Jiang |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XI, 172 p. 87 illus., 74 illus. in color.) |
| Disciplina | 610.28563 |
| 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 | 3-030-32486-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Using Supervised Learning and Guided Monte Carlo Tree Search for Beam Orientation Optimization in Radiation Therapy -- Feasibility of CT-only 3D dose prediction for VMAT prostate plans using deep learning -- Automatically Tracking and Detecting Significant Nodal Mass Shrinkage During Head-and-Neck Radiation Treatment Using Image Saliency -- 4D-CT Deformable Image Registration Using an Unsupervised Deep Convolutional Neural Network -- Toward markerless image-guided radiotherapy using deep learning for prostate cancer -- A Two-Stage Approach for Automated Prostate Lesion Detection and Classification with Mask R-CNN and Weakly Supervised Deep Neural Network -- A Novel Deep Learning Framework for Standardizing the Label of OARs in CT -- Multimodal Volume-Aware Detection and Segmentation for Brain Metastases Radiosurgery -- Voxel-level Radiotherapy Dose Prediction Using Densely Connected Network with Dilated Convolutions -- Online Target Volume Estimation and Prediction From an Interlaced Slice Acquisition - A Manifold Embedding and Learning Approach -- One-dimensional convolutional network for Dosimetry Evaluation at Organs-at-Risk in Esophageal Radiation Treatment Planning -- Unpaired Synthetic Image Generation in Radiology Using GANs -- Deriving lung perfusion directly from CT image using deep convolutional neural network: A preliminary study -- Individualized 3D Dose Distribution Prediction Using Deep Learning -- Deep Generative Model-Driven Multimodal Prostate Segmentation in Radiotherapy -- Dose Distribution Prediction for Optimal Treatment of Modern External Beam Radiation Therapy for Nasopharyngeal Carcinoma -- DeepMCDose: A Deep Learning Method for Efficient Monte Carlo Beamlet Dose Calculation by Predictive Denoising in MR-Guided Radiotherapy -- UC-GAN for MR to CT Image Synthesis -- CBCT-based Synthetic MRI Generation for CBCT-guided Adaptive Radiotherapy -- Cardio-pulmonary Substructure Segmentation of CT images using Convolutional Neural Networks. |
| Record Nr. | UNINA-9910349275203321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||