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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 Intel·ligència artificial en medicina Diagnòstic per la imatge Visió per ordinador Processament de dades |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN |
9783031177217
3031177215 |
| 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 | ||
| ||