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MARC Lista (tabellare)
Brain informatics : 13th International Conference, BI 2020, Padua, Italy, September 19, 2020, Proceedings / / Mufti Mahmud [and three others] (editors)
Brain informatics : 13th International Conference, BI 2020, Padua, Italy, September 19, 2020, Proceedings / / Mufti Mahmud [and three others] (editors)
Edizione [1st ed. 2020.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2020]
Descrizione fisica 1 online resource (XIV, 378 p. 128 illus., 115 illus. in color.)
Disciplina 006.32
Collana Lecture Notes in Computer Science
Soggetto topico Neural networks (Computer science)
Human information processing
Artificial intelligence - Medical applications
ISBN 3-030-59277-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cognitive and Computational Foundations Of Brain Science -- An Adaptive Computational Fear-Avoidance Model Applied to Genito-Pelvic Pain/Penetration Disorder -- Are We Producing Narci-nials? An Adaptive Agent Model for Parental Influence -- A Systematic Assessment of Feature Extraction Methods for Robust Prediction of Neuropsychological Scores from Functional Connectivity Data -- The Effect of Loss-Aversion on Strategic Behaviour of Players in Divergent Interest Tacit Coordination Games -- Effect of the Gamma Entrainment Frequency in Pertinence to Mood, Memory and Cognition -- Investigations of Human Information Processing Systems -- Temporal-Spatial-Spectral Investigation of Brain Network Dynamics in Human Speech Perception -- Precise estimation of Resting State Functional Connectivity Using Empirical Mode Decomposition -- 3D DenseNet Ensemble in 4-Way Classification of Alzheimer's Disease -- Dynamic Functional Connectivity Captures Individuals' Unique Brain Signatures -- Differential Effects of Trait Empathy on Functional Network Centrality -- Classification of PTSD and non-PTSD Using Cortical Structural Measures in Machine Learning Analyses | Preliminary Study of ENIGMA-Psychiatric Genomics Consortium PTSD Workgroup -- Segmentation of Brain Tumor Tissues in Multi-Channel MRI using Convolutional Neural Networks -- Brain Big Data Analytics, Curation and Management -- Resolving Neuroscience Questions Using Ontologies and Templates -- Machine Learning in Analysing Invasively Recorded Neuronal Signals: Available Open Access Data Sources -- Automatic Detection of Epileptic Waves in Electroencephalograms Using Bag of Visual Words and Machine Learning -- UPDRS Label Assignment by Analyzing Accelerometer Sensor Data Collected from Conventional Smartphones -- Effectiveness of Employing Multimodal Signals in Removing Artifacts from Neuronal Signals: An Empirical Analysis -- A Machine Learning Based Fall Detection System for Elderly People with Neurodegenaration Disorders -- Management of Neurodegenarative Diseases using Machine Learning and Internet of Things -- Informatics Paradigms for Brain and Mental Health Research -- A Computational Model for Simultaneous Employment of Multiple Emotion Regulation Strategies -- Deep LSTM Recurrent Neural Network for Anxiety Classification from EEG in Adolescents With Autism -- Improving Alcoholism Diagnosis: Comparing Instance-based Classifiers against Neural Networks for Classifying EEG signal -- A Monitoring System for Patients of Autism Spectrum Disorder using Artificial Intelligence -- Artificial and Internet of Healthcare Things based Alzheimer Care during COVID 19 -- Towards Artificial Intelligence Driven Emotion Aware Fall Monitoring Framework Suitable for Elderly People with Neurological Disorder -- Speech emotion recognition in neurological disorders using Convolutional Neural Network -- Towards Improved Detection of Cognitive Performance using Bidirectional Multi layer Long-Short Term Memory Neural Network -- Brain-Machine Intelligence and Brain-Inspired Computing -- Comparative Study of Wet and Dry Systems on EEG-based Cognitive Tasks -- Recall performance improvement in a bio-inspired model of the mammalian hippocampus -- Canonical retina-to-cortex vision model ready for automatic differentiation -- An Optimized Self-Adjusting Model for EEG Data Analysis in Online Education Processes -- Sequence learning in Associative Neuronal-Astrocytic Networks -- EEG based Sleep-Wake Classification using JOPS Algorithm.
Record Nr. UNISA-996418300903316
Cham, Switzerland : , : Springer, , [2020]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
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Clinical image-based procedures, distributed and collaborative learning, artificial intelligence for combating COVID-19 and secure and privacy-preserving machine learning : 10th Workshop, CLIP 2021, Second Workshop, DCL 2021, First Workshop, LL-COVID19 2021, and First Workshop and Tutorial, PPML 2021, held in conjunction with MICCAI 2021, Strasbourg, France, September 27 and October 1, 2021, proceedings / / Cristina Oyarzun Laura [and three others] editors
Clinical image-based procedures, distributed and collaborative learning, artificial intelligence for combating COVID-19 and secure and privacy-preserving machine learning : 10th Workshop, CLIP 2021, Second Workshop, DCL 2021, First Workshop, LL-COVID19 2021, and First Workshop and Tutorial, PPML 2021, held in conjunction with MICCAI 2021, Strasbourg, France, September 27 and October 1, 2021, proceedings / / Cristina Oyarzun Laura [and three others] editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (201 pages)
Disciplina 616.07540285
Collana Lecture Notes in Computer Science
Soggetto topico Diagnostic imaging - Data processing
Artificial intelligence - Medical applications
ISBN 3-030-90874-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Additional Editors -- CLIP Preface -- CLIP Organization -- DCL Preface -- DCL Organization -- LL-COVID-19 Preface -- LL-COVID-19 Organization -- PPML Preface -- PPML Organization -- Contents -- CLIP -- Intestine Segmentation with Small Computational Cost for Diagnosis Assistance of Ileus and Intestinal Obstruction -- 1 Introduction -- 2 Methods -- 2.1 Overview -- 2.2 Distance Map Estimation for Preventing Incorrect Shortcuts -- 2.3 Graph-Based Segmentation and Visualization -- 3 Experimental Results -- 3.1 Experimental Setup -- 3.2 Evaluations -- 4 Discussion -- 5 Conclusions -- References -- Generation of Patient-Specific, Ligamentoskeletal, Finite Element Meshes for Scoliosis Correction Planning -- 1 Introduction -- 2 Methods -- 2.1 Overview -- 2.2 Patient-Specific, Ligamentoskeletal, Finite Element Mesh Generation -- 3 Results -- 3.1 Datasets -- 3.2 Quantitative Results -- 3.3 Qualitative Results -- 4 Conclusion -- References -- Bayesian Graph Neural Networks for EEG-Based Emotion Recognition -- 1 Introduction -- 2 Methods -- 2.1 Bayesian Graph Neural Networks -- 2.2 Sparse Graph Variational Auto-encoder -- 2.3 Algorithm for BGNN -- 3 Experiments -- 3.1 Datasets -- 3.2 Classification Settings -- 3.3 Results -- 4 Discussion -- 4.1 Ablation Study -- 4.2 Latent Communities -- 5 Conclusions -- References -- ViTBIS: Vision Transformer for Biomedical Image Segmentation -- 1 Introduction -- 2 Related Work -- 2.1 Convolutional Neural Network -- 2.2 Attention Mechanism -- 2.3 Transformers -- 2.4 Background -- 3 Method -- 3.1 Dataset -- 3.2 Network Architecture -- 3.3 Residual Connection -- 3.4 Loss Function -- 3.5 Evaluation Metrics -- 3.6 Implementation Details -- 4 Results -- 4.1 Ablation Studies -- 5 Conclusions -- References -- Attention-Guided Pancreatic Duct Segmentation from Abdominal CT Volumes -- 1 Introduction -- 2 Methods.
2.1 Pancreatic Attention-Guide -- 2.2 Multi-scale Aggregation -- 3 Experiments and Results -- 3.1 Dataset and Settings -- 3.2 Segmentation Results and Discussion -- 4 Conclusion -- References -- Development of the Next Generation Hand-Held Doppler with Waveform Phasicity Predictive Capabilities Using Deep Learning -- 1 Introduction -- 1.1 Background -- 1.2 Innovation -- 1.3 Implementation Summary -- 2 Methods -- 2.1 Data Preparation -- 2.2 Model Development -- 2.3 Hardware Platform -- 3 Results -- 3.1 Baseline Validation -- 3.2 Manual Experiment -- 4 Conclusion -- References -- Learning from Mistakes: An Error-Driven Mechanism to Improve Segmentation Performance Based on Expert Feedback -- 1 Introduction -- 2 Data -- 3 Method -- 4 Experiments and Results -- 4.1 Proof of Concept: Recovering Systematic Errors -- 4.2 Clinical Application: Predicting Expert Corrections -- 5 Discussion and Conclusion -- References -- TMJOAI: An Artificial Web-Based Intelligence Tool for Early Diagnosis of the Temporomandibular Joint Osteoarthritis -- 1 Introduction -- 2 Dataset -- 3 Proposed Methods -- 3.1 Feature Selection -- 3.2 Comparison of Multiple Machine Learning Algorithms -- 3.3 Histogram Matching -- 4 Experimental Results -- 4.1 Experiments -- 4.2 Algorithm Comparison Results -- 4.3 Histogram Matching and Mandibular Fossa Features Results -- 4.4 Deployment -- 5 Conclusion -- References -- COVID-19 Infection Segmentation from Chest CT Images Based on Scale Uncertainty -- 1 Introduction -- 2 Method -- 2.1 Infection Region Segmentation by ISNet -- 2.2 Scale Uncertainty-Aware Prediction Aggregation -- 3 Experiments and Results -- 3.1 Ablation and Comparative Study of ISNet -- 3.2 Segmentation by Aggregation FCN -- 4 Discussion and Conclusions -- References -- DCL -- Multi-task Federated Learning for Heterogeneous Pancreas Segmentation -- 1 Introduction -- 2 Methods.
2.1 FedAvg -- 2.2 FedProx -- 2.3 Dynamic Task Prioritization -- 2.4 Dynamic Weight Averaging -- 3 Experiments and Results -- 3.1 Datasets -- 3.2 Experimental Details -- 3.3 Results -- 4 Discussion -- 5 Conclusion -- References -- Federated Learning in the Cloud for Analysis of Medical Images - Experience with Open Source Frameworks -- 1 Introduction -- 2 Related Work -- 3 Dataset Used in Evaluation -- 4 Overview of Available Open Source Frameworks for FL -- 4.1 TensorFlow Federated -- 4.2 PySyft -- 4.3 Flower -- 5 Experiment Setup -- 6 Results -- 6.1 Results for EfficientNetB0 Architecture -- 6.2 Results for ResNet50 Architecture -- 7 Conclusion -- References -- On the Fairness of Swarm Learning in Skin Lesion Classification -- 1 Introduction -- 2 Related Works -- 2.1 Collaborative Learning and Their Application on Healthcare -- 2.2 Security and Privacy of Federated Learning -- 2.3 Fairness -- 3 Problem Setting and Methods -- 3.1 Problem Setting -- 3.2 Swarm Learning -- 3.3 Local and Centralized Training -- 3.4 Fairness Definition and Metrics -- 4 Experiment and Results -- 4.1 Dataset -- 4.2 Implementation Details -- 4.3 Biases in Models Trained with Different Strategies -- 5 Discussion and Conclusion -- References -- LL-COVID19 -- Lessons Learned from the Development and Application of Medical Imaging-Based AI Technologies for Combating COVID-19: Why Discuss, What Next -- 1 Introduction -- 2 Data Definition -- 3 Data Availability -- 4 Translational Research -- 5 Summary and Next Steps -- References -- The Role of Pleura and Adipose in Lung Ultrasound AI -- 1 Introduction -- 2 Methodology -- 2.1 SubQ Masking -- 2.2 Data -- 2.3 Architecture -- 2.4 Training Strategy -- 3 Experiments -- 4 Results and Discussions -- 5 Conclusion -- References -- DuCN: Dual-Children Network for Medical Diagnosis and Similar Case Recommendation Towards COVID-19.
1 Introduction -- 2 Method -- 2.1 Proposed Model -- 2.2 Dual-Children Network -- 2.3 Loss Functions -- 3 Experiments and Results -- 3.1 Dataset and Experiments -- 3.2 Results -- 3.3 Ablation Study -- 4 Discussion and Conclusions -- References -- PPML -- Data Imputation and Reconstruction of Distributed Parkinson's Disease Clinical Assessments: A Comparative Evaluation of Two Aggregation Algorithms -- 1 Introduction -- 1.1 Clinical Assessments and Challenges -- 1.2 Contributions -- 2 Related Work -- 3 Methods -- 3.1 Data -- 3.2 Model Setup -- 3.3 Aggregation Algorithms -- 4 Experimental Results -- 4.1 Effect of Number of Missing Modalities During Training -- 4.2 Effect of Number of Missing Values During Evaluation -- 5 Discussion and Conclusion -- References -- Defending Medical Image Diagnostics Against Privacy Attacks Using Generative Methods: Application to Retinal Diagnostics -- 1 Introduction -- 2 Background -- 3 Prior Work -- 4 Methodology -- 4.1 Threat Model -- 4.2 Approach for Data Producer to Defend Privacy -- 4.3 Novel Metric Balancing Utility and Privacy -- 5 Experiments -- 5.1 Dataset -- 5.2 Results -- 6 Discussion and Limitations -- 7 Conclusion -- References -- Author Index.
Record Nr. UNISA-996464421703316
Cham, Switzerland : , : Springer, , [2021]
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Lo trovi qui: Univ. di Salerno
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Clinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging : 12th International Workshop, CLIP 2023 1st International Workshop, FAIMI 2023 and 2nd International Workshop, EPIMI 2023 Vancouver, BC, Canada, October 8 and October 12, 2023 Proceedings / / Stefan Wesarg [and nine others], editors
Clinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging : 12th International Workshop, CLIP 2023 1st International Workshop, FAIMI 2023 and 2nd International Workshop, EPIMI 2023 Vancouver, BC, Canada, October 8 and October 12, 2023 Proceedings / / Stefan Wesarg [and nine others], editors
Edizione [First edition.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, Springer Nature Switzerland AG, , [2023]
Descrizione fisica 1 online resource (327 pages)
Disciplina 610.28563
Collana Lecture Notes in Computer Science Series
Soggetto topico Artificial intelligence - Medical applications
Diagnostic imaging
Diagnostic imaging - Data processing
ISBN 3-031-45249-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Additional Editors -- CLIP Preface -- CLIP Organization -- FAIMI Preface -- FAIMI Organization -- EPIMI Preface -- EPIMI Organization -- Contents -- CLIP -- Automated Hand Joint Classification of Psoriatic Arthritis Patients Using Routinely Acquired Near Infrared Fluorescence Optical Imaging -- 1 Introduction -- 2 Background -- 3 Method -- 4 Results -- 5 Discussion and Future Work -- References -- Automatic Neurocranial Landmarks Detection from Visible Facial Landmarks Leveraging 3D Head Priors -- 1 Introduction -- 2 Methods -- 2.1 Datasets and Preprocessing -- 2.2 Models Training and Evaluation -- 3 Experimental Results -- 3.1 Neurocranial Landmark Coordinates Prediction -- 3.2 3DMM Validation -- 3.3 Ablation Study -- 4 Discussion and Conclusions -- References -- Subject-Specific Modelling of Knee Joint Motion for Routine Pre-operative Planning -- 1 Introduction -- 2 Method -- 2.1 Contact Surface Model of PF and TF Joint -- 2.2 Computation of Knee Flexion Angle -- 2.3 Matching Tibia and Patella Poses -- 3 Experiments and Discussions -- 3.1 Evaluation of Generated Patella and Tibia Poses -- 3.2 Evaluation of Tibia and Patella Pose Matching -- 4 Conclusion -- References -- Towards Fine-Grained Polyp Segmentation and Classification -- 1 Introduction -- 2 Method -- 2.1 Swin Transformer Encoder -- 2.2 Multi-Scale Feature Enhancement -- 2.3 Patch-Expanding Decoder -- 2.4 Upsample Head -- 2.5 Loss Function -- 3 PolypSegm-ASH Dataset -- 4 Results -- 4.1 Experiments on PolypSegm-ASH -- 4.2 Experiments on Binary Polyp Segmentation -- 4.3 Ablation Study. Effect of Up-Samples Before Predictions -- 5 Conclusion -- References -- Automated Orientation and Registration of Cone-Beam Computed Tomography Scans -- 1 Introduction -- 2 Materials -- 3 Proposed Method -- 3.1 Automated Standardized Orientation (ASO) -- 3.2 Automated Registration (AReg).
3.3 Evaluation Metrics -- 3.4 Implementation -- 4 Results -- 4.1 Orientation -- 4.2 Registration -- 5 Discussion -- 6 Conclusion -- A Appendix -- References -- Deep Learning-Based Fast MRI Reconstruction: Improving Generalization for Clinical Translation -- 1 Introduction -- 2 Methods -- 2.1 Background -- 2.2 Physically-Primed DNN for MRI Reconstruction -- 3 Experiments -- 3.1 Dataset -- 3.2 Experimental Methodology -- 3.3 Results -- 4 Conclusions -- References -- Uncertainty Based Border-Aware Segmentation Network for Deep Caries -- 1 Introduction -- 2 Related Work -- 2.1 Dental Caries Image Segmentation -- 2.2 Uncertainty Quantification -- 3 Method -- 3.1 Border-Aware Network Using SDF -- 3.2 Uncertainty Based Caries Segmentation -- 4 Experiments and Discussion -- 4.1 Dataset and Settings -- 4.2 Verification of SDF Effectiveness -- 4.3 Verification of Model Robustness -- 5 Conclusion -- References -- An Efficient and Accurate Neural Network Tool for Finding Correlation Between Gene Expression and Histological Images -- 1 Introduction -- 2 Methodology -- 2.1 Data -- 2.2 Label Generation -- 2.3 CNN Training and Testing -- 2.4 Significance Testing and Gene Set Analysis -- 3 Results -- 3.1 Resnet Comparison -- 3.2 Significant Genes and Pathways -- 3.3 Correlations Between Model Performance and Data Properties -- 3.4 Comparison of Findings with Other Methodologies -- 4 Conclusions -- References -- FAIMI -- De-identification and Obfuscation of Gender Attributes from Retinal Scans -- 1 Introduction -- 1.1 Differential Privacy for Image Obfuscation -- 1.2 Deep Learning for Diabetic Retinopathy and Sex Classification -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 Pre-processing -- 2.3 De-identification Framework -- 2.4 Evaluation Framework -- 3 Results -- 3.1 Full Image Snow Results -- 3.2 VS-Snow Results -- 4 Discussion -- 4.1 Privacy-Utility Tradeoff.
4.2 Importance of Vasculature -- 4.3 Limitations and Future Work -- References -- Unveiling Fairness Biases in Deep Learning-Based Brain MRI Reconstruction -- 1 Introduction -- 2 Background -- 2.1 Fairness Definitions -- 2.2 Source of Bias -- 3 Methods -- 4 Experimental Analysis -- 4.1 Dataset and Pre-processing -- 4.2 Implementation Details -- 4.3 Results -- 5 Discussion -- 6 Conclusion -- References -- Brain Matters: Exploring Bias in AI for Neuroimaging Research -- 1 Introduction -- 2 Current Problems -- 2.1 Structural Problems -- 2.2 Specific Biases -- 3 Mitigation Strategies -- 3.1 Collect More Representative Data -- 3.2 Share and Collaborate -- 3.3 Reduce Reliance on Inaccessible Data Collection Methods -- 3.4 Develop Both Generic and Specific Models and Employ Transfer Learning -- 3.5 Consider the Use of Data Augmentation -- 3.6 Raise Awareness of Bias and Engage in PPI -- 4 Limitations -- 5 Conclusion -- References -- Bias in Unsupervised Anomaly Detection in Brain MRI -- 1 Introduction -- 2 Materials and Methods -- 3 Experiments and Results -- 3.1 Baseline Performance -- 3.2 Impact of Bias -- 3.3 Sources of Bias -- 4 Conclusion -- References -- Towards Unraveling Calibration Biases in Medical Image Analysis -- 1 Introduction -- 2 Numerical Experiments on Real Data -- 2.1 Data -- 2.2 Model Training -- 2.3 Platt Scaling -- 2.4 Performance Evaluation -- 2.5 Results -- 3 Synthetic Experiments -- 3.1 Data -- 3.2 Performance Evaluation -- 3.3 Results -- 4 Discussion -- References -- Are Sex-Based Physiological Differences the Cause of Gender Bias for Chest X-Ray Diagnosis? -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Datasets -- 3.2 Sampling Strategy -- 3.3 Experimental Settings -- 4 Results -- 4.1 Model Performance Across Diseases, Gender Ratios, and Datasets -- 4.2 Comparison of Different Sampling Strategies.
4.3 Breast Cropping Does Not Mitigate Gender Biases -- 4.4 Dataset Bias v.s. Model Bias -- 5 Discussion and Conclusions -- References -- Bayesian Uncertainty-Weighted Loss for Improved Generalisability on Polyp Segmentation Task -- 1 Introduction -- 2 Related Work -- 3 Method -- 4 Experiments and Results -- 4.1 Dataset and Experimental Setup -- 4.2 Results -- 5 Conclusion -- References -- Mitigating Bias in MRI-Based Alzheimer's Disease Classifiers Through Pruning of Deep Neural Networks -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data and Preprocess -- 2.2 Debiasing by Pruning -- 3 Experiment -- 3.1 Implementation and Evaluation -- 3.2 Comparison -- 4 Result -- 5 Discussion and Conclusion -- References -- Auditing Unfair Biases in CNN-Based Diagnosis of Alzheimer's Disease -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data Description and Preprocessing -- 2.2 Models -- 2.3 Bias Evaluation Metrics -- 3 Results and Discussion -- 3.1 Auditing Fairness with Respect to Model Performance -- 3.2 Auditing Fairness with Respect to Model Calibration -- 4 Conclusions -- References -- Distributionally Robust Optimization and Invariant Representation Learning for Addressing Subgroup Underrepresentation: Mechanisms and Limitations -- 1 Introduction -- 2 Assessing Debiasing Mechanisms -- 2.1 Methodology -- 2.2 Experiments and Results -- 3 Improving the Debiasing of Spurious Correlations -- References -- Analysing Race and Sex Bias in Brain Age Prediction -- 1 Introduction -- 2 Materials and Methods -- 2.1 Bias Analysis -- 3 Results -- 4 Discussion and Conclusion -- A Appendix -- References -- Studying the Effects of Sex-Related Differences on Brain Age Prediction Using Brain MR Imaging -- 1 Introduction -- 2 Materials and Methods -- 2.1 Brain MR Datasets -- 2.2 Pre-processing -- 2.3 Brain Age Prediction Task -- 2.4 Grad-CAM Interpretability.
2.5 Experimental Setting -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- An Investigation into the Impact of Deep Learning Model Choice on Sex and Race Bias in Cardiac MR Segmentation -- 1 Introduction -- 2 Materials -- 3 Methods -- 3.1 Dataset Sampling -- 3.2 Model Architecture and Implementation -- 3.3 Model Evaluation -- 4 Results -- 5 Discussion -- References -- An Investigation into Race Bias in Random Forest Models Based on Breast DCE-MRI Derived Radiomics Features -- 1 Introduction -- 2 Materials -- 3 Methods -- 4 Experiments and Results -- 4.1 Race Classification -- 4.2 Bias Analysis -- 4.3 Covariate Analysis -- 5 Discussion and Conclusions -- References -- How You Split Matters: Data Leakage and Subject Characteristics Studies in Longitudinal Brain MRI Analysis -- 1 Introduction -- 2 Methods -- 2.1 Data Collection and Processing -- 2.2 Training Setup -- 2.3 Evaluation Scheme -- 3 Result -- 4 Discussion and Conclusion -- References -- Revisiting Skin Tone Fairness in Dermatological Lesion Classification -- 1 Introduction -- 2 Methods and Materials -- 2.1 Dataset -- 2.2 Evaluation of Skin Lesion Classification -- 2.3 Skin Tone Estimation -- 3 Experiments and Results -- 3.1 Comparison of ITA Estimation Methods -- 3.2 Fairness Analysis -- 3.3 Simulated Data Shifts -- 4 Conclusions -- References -- A Study of Age and Sex Bias in Multiple Instance Learning Based Classification of Acute Myeloid Leukemia Subtypes -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data -- 2.2 Multiple Instance Learning -- 3 Experiments -- 3.1 Sex Bias -- 3.2 Age Bias -- 4 Results -- 4.1 Sex Bias -- 4.2 Age Bias -- 5 Discussion -- References -- Unsupervised Bias Discovery in Medical Image Segmentation -- 1 Introduction -- 2 Related Work -- 3 Unsupervised Bias Discovery via Reverse Classification Accuracy -- 4 Experiments and Discussion.
4.1 Synthetic Experiment: Validating RCA for UBD.
Record Nr. UNINA-9910747597703321
Cham, Switzerland : , : Springer, Springer Nature Switzerland AG, , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Clinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging : 12th International Workshop, CLIP 2023 1st International Workshop, FAIMI 2023 and 2nd International Workshop, EPIMI 2023 Vancouver, BC, Canada, October 8 and October 12, 2023 Proceedings / / Stefan Wesarg [and nine others], editors
Clinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging : 12th International Workshop, CLIP 2023 1st International Workshop, FAIMI 2023 and 2nd International Workshop, EPIMI 2023 Vancouver, BC, Canada, October 8 and October 12, 2023 Proceedings / / Stefan Wesarg [and nine others], editors
Edizione [First edition.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, Springer Nature Switzerland AG, , [2023]
Descrizione fisica 1 online resource (327 pages)
Disciplina 610.28563
Collana Lecture Notes in Computer Science Series
Soggetto topico Artificial intelligence - Medical applications
Diagnostic imaging
Diagnostic imaging - Data processing
ISBN 3-031-45249-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Additional Editors -- CLIP Preface -- CLIP Organization -- FAIMI Preface -- FAIMI Organization -- EPIMI Preface -- EPIMI Organization -- Contents -- CLIP -- Automated Hand Joint Classification of Psoriatic Arthritis Patients Using Routinely Acquired Near Infrared Fluorescence Optical Imaging -- 1 Introduction -- 2 Background -- 3 Method -- 4 Results -- 5 Discussion and Future Work -- References -- Automatic Neurocranial Landmarks Detection from Visible Facial Landmarks Leveraging 3D Head Priors -- 1 Introduction -- 2 Methods -- 2.1 Datasets and Preprocessing -- 2.2 Models Training and Evaluation -- 3 Experimental Results -- 3.1 Neurocranial Landmark Coordinates Prediction -- 3.2 3DMM Validation -- 3.3 Ablation Study -- 4 Discussion and Conclusions -- References -- Subject-Specific Modelling of Knee Joint Motion for Routine Pre-operative Planning -- 1 Introduction -- 2 Method -- 2.1 Contact Surface Model of PF and TF Joint -- 2.2 Computation of Knee Flexion Angle -- 2.3 Matching Tibia and Patella Poses -- 3 Experiments and Discussions -- 3.1 Evaluation of Generated Patella and Tibia Poses -- 3.2 Evaluation of Tibia and Patella Pose Matching -- 4 Conclusion -- References -- Towards Fine-Grained Polyp Segmentation and Classification -- 1 Introduction -- 2 Method -- 2.1 Swin Transformer Encoder -- 2.2 Multi-Scale Feature Enhancement -- 2.3 Patch-Expanding Decoder -- 2.4 Upsample Head -- 2.5 Loss Function -- 3 PolypSegm-ASH Dataset -- 4 Results -- 4.1 Experiments on PolypSegm-ASH -- 4.2 Experiments on Binary Polyp Segmentation -- 4.3 Ablation Study. Effect of Up-Samples Before Predictions -- 5 Conclusion -- References -- Automated Orientation and Registration of Cone-Beam Computed Tomography Scans -- 1 Introduction -- 2 Materials -- 3 Proposed Method -- 3.1 Automated Standardized Orientation (ASO) -- 3.2 Automated Registration (AReg).
3.3 Evaluation Metrics -- 3.4 Implementation -- 4 Results -- 4.1 Orientation -- 4.2 Registration -- 5 Discussion -- 6 Conclusion -- A Appendix -- References -- Deep Learning-Based Fast MRI Reconstruction: Improving Generalization for Clinical Translation -- 1 Introduction -- 2 Methods -- 2.1 Background -- 2.2 Physically-Primed DNN for MRI Reconstruction -- 3 Experiments -- 3.1 Dataset -- 3.2 Experimental Methodology -- 3.3 Results -- 4 Conclusions -- References -- Uncertainty Based Border-Aware Segmentation Network for Deep Caries -- 1 Introduction -- 2 Related Work -- 2.1 Dental Caries Image Segmentation -- 2.2 Uncertainty Quantification -- 3 Method -- 3.1 Border-Aware Network Using SDF -- 3.2 Uncertainty Based Caries Segmentation -- 4 Experiments and Discussion -- 4.1 Dataset and Settings -- 4.2 Verification of SDF Effectiveness -- 4.3 Verification of Model Robustness -- 5 Conclusion -- References -- An Efficient and Accurate Neural Network Tool for Finding Correlation Between Gene Expression and Histological Images -- 1 Introduction -- 2 Methodology -- 2.1 Data -- 2.2 Label Generation -- 2.3 CNN Training and Testing -- 2.4 Significance Testing and Gene Set Analysis -- 3 Results -- 3.1 Resnet Comparison -- 3.2 Significant Genes and Pathways -- 3.3 Correlations Between Model Performance and Data Properties -- 3.4 Comparison of Findings with Other Methodologies -- 4 Conclusions -- References -- FAIMI -- De-identification and Obfuscation of Gender Attributes from Retinal Scans -- 1 Introduction -- 1.1 Differential Privacy for Image Obfuscation -- 1.2 Deep Learning for Diabetic Retinopathy and Sex Classification -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 Pre-processing -- 2.3 De-identification Framework -- 2.4 Evaluation Framework -- 3 Results -- 3.1 Full Image Snow Results -- 3.2 VS-Snow Results -- 4 Discussion -- 4.1 Privacy-Utility Tradeoff.
4.2 Importance of Vasculature -- 4.3 Limitations and Future Work -- References -- Unveiling Fairness Biases in Deep Learning-Based Brain MRI Reconstruction -- 1 Introduction -- 2 Background -- 2.1 Fairness Definitions -- 2.2 Source of Bias -- 3 Methods -- 4 Experimental Analysis -- 4.1 Dataset and Pre-processing -- 4.2 Implementation Details -- 4.3 Results -- 5 Discussion -- 6 Conclusion -- References -- Brain Matters: Exploring Bias in AI for Neuroimaging Research -- 1 Introduction -- 2 Current Problems -- 2.1 Structural Problems -- 2.2 Specific Biases -- 3 Mitigation Strategies -- 3.1 Collect More Representative Data -- 3.2 Share and Collaborate -- 3.3 Reduce Reliance on Inaccessible Data Collection Methods -- 3.4 Develop Both Generic and Specific Models and Employ Transfer Learning -- 3.5 Consider the Use of Data Augmentation -- 3.6 Raise Awareness of Bias and Engage in PPI -- 4 Limitations -- 5 Conclusion -- References -- Bias in Unsupervised Anomaly Detection in Brain MRI -- 1 Introduction -- 2 Materials and Methods -- 3 Experiments and Results -- 3.1 Baseline Performance -- 3.2 Impact of Bias -- 3.3 Sources of Bias -- 4 Conclusion -- References -- Towards Unraveling Calibration Biases in Medical Image Analysis -- 1 Introduction -- 2 Numerical Experiments on Real Data -- 2.1 Data -- 2.2 Model Training -- 2.3 Platt Scaling -- 2.4 Performance Evaluation -- 2.5 Results -- 3 Synthetic Experiments -- 3.1 Data -- 3.2 Performance Evaluation -- 3.3 Results -- 4 Discussion -- References -- Are Sex-Based Physiological Differences the Cause of Gender Bias for Chest X-Ray Diagnosis? -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Datasets -- 3.2 Sampling Strategy -- 3.3 Experimental Settings -- 4 Results -- 4.1 Model Performance Across Diseases, Gender Ratios, and Datasets -- 4.2 Comparison of Different Sampling Strategies.
4.3 Breast Cropping Does Not Mitigate Gender Biases -- 4.4 Dataset Bias v.s. Model Bias -- 5 Discussion and Conclusions -- References -- Bayesian Uncertainty-Weighted Loss for Improved Generalisability on Polyp Segmentation Task -- 1 Introduction -- 2 Related Work -- 3 Method -- 4 Experiments and Results -- 4.1 Dataset and Experimental Setup -- 4.2 Results -- 5 Conclusion -- References -- Mitigating Bias in MRI-Based Alzheimer's Disease Classifiers Through Pruning of Deep Neural Networks -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data and Preprocess -- 2.2 Debiasing by Pruning -- 3 Experiment -- 3.1 Implementation and Evaluation -- 3.2 Comparison -- 4 Result -- 5 Discussion and Conclusion -- References -- Auditing Unfair Biases in CNN-Based Diagnosis of Alzheimer's Disease -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data Description and Preprocessing -- 2.2 Models -- 2.3 Bias Evaluation Metrics -- 3 Results and Discussion -- 3.1 Auditing Fairness with Respect to Model Performance -- 3.2 Auditing Fairness with Respect to Model Calibration -- 4 Conclusions -- References -- Distributionally Robust Optimization and Invariant Representation Learning for Addressing Subgroup Underrepresentation: Mechanisms and Limitations -- 1 Introduction -- 2 Assessing Debiasing Mechanisms -- 2.1 Methodology -- 2.2 Experiments and Results -- 3 Improving the Debiasing of Spurious Correlations -- References -- Analysing Race and Sex Bias in Brain Age Prediction -- 1 Introduction -- 2 Materials and Methods -- 2.1 Bias Analysis -- 3 Results -- 4 Discussion and Conclusion -- A Appendix -- References -- Studying the Effects of Sex-Related Differences on Brain Age Prediction Using Brain MR Imaging -- 1 Introduction -- 2 Materials and Methods -- 2.1 Brain MR Datasets -- 2.2 Pre-processing -- 2.3 Brain Age Prediction Task -- 2.4 Grad-CAM Interpretability.
2.5 Experimental Setting -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- An Investigation into the Impact of Deep Learning Model Choice on Sex and Race Bias in Cardiac MR Segmentation -- 1 Introduction -- 2 Materials -- 3 Methods -- 3.1 Dataset Sampling -- 3.2 Model Architecture and Implementation -- 3.3 Model Evaluation -- 4 Results -- 5 Discussion -- References -- An Investigation into Race Bias in Random Forest Models Based on Breast DCE-MRI Derived Radiomics Features -- 1 Introduction -- 2 Materials -- 3 Methods -- 4 Experiments and Results -- 4.1 Race Classification -- 4.2 Bias Analysis -- 4.3 Covariate Analysis -- 5 Discussion and Conclusions -- References -- How You Split Matters: Data Leakage and Subject Characteristics Studies in Longitudinal Brain MRI Analysis -- 1 Introduction -- 2 Methods -- 2.1 Data Collection and Processing -- 2.2 Training Setup -- 2.3 Evaluation Scheme -- 3 Result -- 4 Discussion and Conclusion -- References -- Revisiting Skin Tone Fairness in Dermatological Lesion Classification -- 1 Introduction -- 2 Methods and Materials -- 2.1 Dataset -- 2.2 Evaluation of Skin Lesion Classification -- 2.3 Skin Tone Estimation -- 3 Experiments and Results -- 3.1 Comparison of ITA Estimation Methods -- 3.2 Fairness Analysis -- 3.3 Simulated Data Shifts -- 4 Conclusions -- References -- A Study of Age and Sex Bias in Multiple Instance Learning Based Classification of Acute Myeloid Leukemia Subtypes -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data -- 2.2 Multiple Instance Learning -- 3 Experiments -- 3.1 Sex Bias -- 3.2 Age Bias -- 4 Results -- 4.1 Sex Bias -- 4.2 Age Bias -- 5 Discussion -- References -- Unsupervised Bias Discovery in Medical Image Segmentation -- 1 Introduction -- 2 Related Work -- 3 Unsupervised Bias Discovery via Reverse Classification Accuracy -- 4 Experiments and Discussion.
4.1 Synthetic Experiment: Validating RCA for UBD.
Record Nr. UNISA-996558469503316
Cham, Switzerland : , : Springer, Springer Nature Switzerland AG, , [2023]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
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Clinical neurotechnology meets artificial intelligence : philosophical, ethical, legal and social implications / / edited by Orsolya Friedrich [and four others]
Clinical neurotechnology meets artificial intelligence : philosophical, ethical, legal and social implications / / edited by Orsolya Friedrich [and four others]
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (X, 226 p. 3 illus., 1 illus. in color.)
Disciplina 610.28563
Collana Advances in Neuroethics
Soggetto topico Artificial intelligence - Medical applications
Neurologia
Intel·ligència artificial en medicina
Soggetto genere / forma Llibres electrònics
ISBN 3-030-64590-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto I Neurotechnologies and AI: State of the Art: Non-invasive Brain-Computer Communication: from basic science to application -- Towards a framework for the responsible development and use of intelligent neurotechnology -- II Philosophical aspects: Assessing human responsibility for actions mediated by neurotechnology: Complications, confusions and questions -- Will the real cyborg please stand up? -- Consenti et faciam quod vis? Anthropological and ethical implications of consenting to Black Box-algorithms -- Speech BCIs -- Agency, Responsibility, Selves, and the Mechanical Mind -- TBA (project member) -- III Legal aspects: Diffusion on both ends - legal protection and criminalisation in neurotechnological uncertainty -- Brain-computer interfaces and the law -- IV Social aspects: Eric Racine (University of Montréal) & Matthew Sample (University of Montréal): Pragmatism in a Digital Society: Unpacking the (In)Significance of Emerging Digital Technologies for Academics and Their Publics -- The Utopian Mundane: A Design Perspective on Future Technologies -- V Applications: TBA (invited for neurotech and nursing) -- Ethical implications of medical BCIs -- Subjectivation by Neurotechnologies: Some Irritating Implications -- Ethics of neuroprothetics -- Security Implications of Neurotechnology & Artificial Intelligence -- Connecting Brain and Machine: When your mind can directly interact with technology -- Wired emotions: affective brain-computer interfaces and beyond -- TBA (invited for brain hacking) -- Ethics and Brain-Computer Interfaces: A Mixed-Methods-Study with Healthy Users -- In your (inter-)face! Between participation and competitive interest – Findings from an empirical interview study with brain-computer interface users -- Philosophical and ethical implications of Brain-to-Brain Interfaces.
Record Nr. UNINA-9910484285203321
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Cognitive informatics in health and biomedicine : case studies on critical care, complexity and errors / / Vimla L. Patel, David R. Kaufman, Trevor Cohen, editors
Cognitive informatics in health and biomedicine : case studies on critical care, complexity and errors / / Vimla L. Patel, David R. Kaufman, Trevor Cohen, editors
Edizione [1st ed. 2014.]
Pubbl/distr/stampa London : , : Springer, , [2014]
Descrizione fisica 1 online resource (xxi, 505 pages) : illustrations (some color)
Disciplina 004.019
610.28
Collana Health Informatics
Soggetto topico Artificial intelligence - Medical applications
Medical informatics
Neural networks (Computer science)
ISBN 1-4471-5490-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Foreword -- Preface -- Introduction -- Paradigm Shift in Conceptualizing Error -- Analysis of Error based on Laboratory Studies -- Team Decision Making and the Analysis of Error -- Influence of Training on Error Detection in Simulated Clinical Rounds -- Opportunistic Decision Making and Workflow Patterns -- Decision Making and Deviations from Protocol in Trauma -- Effect of Information Seeking Activities on Clinical Decision Making -- Investigating Communication Complexity and Errors: A Continuity of Care based Approach -- Bridging Gaps in Transitions of Care: Design and Evaluation of Handoff Intervention Tool -- Driven to Distraction: Classifying Interruptions in Intensive Care -- Shared Mental Models in Team Handoff -- Enhancing Communication and Improving Coordination in ICU -- The interplay of organizational structure and communication practices -- Activity Prediction and Automated Workflow Modeling using RFID Sensors -- Sensor-based Tracking of Team Interactions and Clinical Workflow -- Work Domains, Complexity and Situation Awareness in the ED -- A framework for understanding error and complexity in critical care -- Communication and Complexity: Negotiating transitions in shift work and the coordination of patient care -- Learning and Competency: Role of Cognition and Error in the Complex Workplace -- A Framework for Complexity and Cognition in Technology-Rich Clinical Settings -- Clinical Practice -- Education and Training -- Biomedical Informatics -- Epilogue.
Record Nr. UNINA-9910300076503321
London : , : Springer, , [2014]
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Cognitive internet of medical things for smart healthcare : services and applications / / Aboul Ella Hassanien [and four others], editors
Cognitive internet of medical things for smart healthcare : services and applications / / Aboul Ella Hassanien [and four others], editors
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (XIII, 227 p. 97 illus., 82 illus. in color.)
Disciplina 004.678
Collana Studies in systems, decision and control
Soggetto topico Internet of things
Artificial intelligence - Medical applications
Cloud computing
ISBN 3-030-55833-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto A Review of Applications, Security and Challenges of Internet of Medical Things -- Applications and Challenges of Cloud Integrated IoMT -- An Effective Fuzzy Logic Based Clustering Scheme for Edge-Computing Based Internet of Medical Things Systems -- An IOT Based Medical Tracking System (IMTS) and Prediction with Probability of Infection.
Record Nr. UNINA-9910483743603321
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Computational analysis and deep learning for medical care : principles, methods, and applications / / edited by Amit Kumar Tyagi
Computational analysis and deep learning for medical care : principles, methods, and applications / / edited by Amit Kumar Tyagi
Pubbl/distr/stampa Hoboken, New Jersey ; ; Beverly, Massachusetts : , : Wiley : , : Scrivener Publishing, , [2021]
Descrizione fisica 1 online resource (528 pages)
Disciplina 610.285
Soggetto topico Artificial intelligence - Medical applications
Soggetto genere / forma Electronic books.
ISBN 1-119-78573-1
1-119-78575-8
1-119-78574-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910555180103321
Hoboken, New Jersey ; ; Beverly, Massachusetts : , : Wiley : , : Scrivener Publishing, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Computational analysis and deep learning for medical care : principles, methods, and applications / / edited by Amit Kumar Tyagi
Computational analysis and deep learning for medical care : principles, methods, and applications / / edited by Amit Kumar Tyagi
Pubbl/distr/stampa Hoboken, NJ : , : Wiley, , [2021]
Descrizione fisica 1 online resource (528 pages)
Disciplina 610.285
Soggetto topico Artificial intelligence - Medical applications
ISBN 1-119-78573-1
1-119-78575-8
1-119-78574-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910830383103321
Hoboken, NJ : , : Wiley, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Computational intelligence and healthcare informatics / / edited by Om Prakash Jena [and three others]
Computational intelligence and healthcare informatics / / edited by Om Prakash Jena [and three others]
Pubbl/distr/stampa Beverly, Massachusetts ; ; Hoboken, New Jersey : , : Scrivener Publishing : , : Wiley, , [2021]
Descrizione fisica 1 online resource (448 pages)
Disciplina 610.285
Collana Machine learning in biomedical science and healthcare informatics
Soggetto topico Artificial intelligence - Medical applications
Soggetto genere / forma Electronic books.
ISBN 1-119-81869-9
1-119-81871-0
1-119-81870-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910555162703321
Beverly, Massachusetts ; ; Hoboken, New Jersey : , : Scrivener Publishing : , : Wiley, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
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