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Autor Adeli, Ehsan |
Documentos disponibles escritos por este autor (5)
Crear una solicitud de compra Refinar búsquedaAdolescent Brain Cognitive Development Neurocognitive Prediction / Pohl, Kilian M. ; Thompson, Wesley K. ; Adeli, Ehsan ; Linguraru, Marius George
TÃtulo : Adolescent Brain Cognitive Development Neurocognitive Prediction : First Challenge, ABCD-NP 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings Tipo de documento: documento electrónico Autores: Pohl, Kilian M., ; Thompson, Wesley K., ; Adeli, Ehsan, ; Linguraru, Marius George, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2019 Número de páginas: XI, 188 p. 57 ilustraciones, 49 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-31901-4 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Palabras clave: Visión por computador Aprendizaje automático Informática Estadistica matematica Procesamiento de datos Probabilidad y EstadÃstica en Informática MinerÃa de datos y descubrimiento de conocimientos Clasificación: Resumen: Este libro constituye las actas arbitradas del Primer DesafÃo en Predicción Neurocognitiva del Desarrollo Cognitivo del Cerebro de Adolescentes, ABCD-NP 2019, celebrado junto con MICCAI 2019, en Shenzhen, China, en octubre de 2019. Se revisaron cuidadosamente 29 presentaciones y 24 de ellas fueron aceptadas. . Algunas de las 24 presentaciones se fusionaron y dieron como resultado los 21 artÃculos que se presentan en este libro. Los artÃculos exploran métodos para predecir la inteligencia fluida a partir de resonancia magnética ponderada en T1 de 8669 niños (de 9 a 10 años de edad) reclutados por el estudio Adolescent Brain Cognitive Development Study (ABCD); el mayor estudio a largo plazo sobre el desarrollo del cerebro y la salud infantil en los Estados Unidos hasta la fecha. Nota de contenido: A Combined Deep Learning-Gradient Boosting Machine Framework for Fluid Intelligence Prediction -- Predicting Fluid Intelligence of Children using T1-weighted MR Images and a StackNet -- Deep Learning vs. Classical Machine Learning: A Comparison of Methods for Fluid Intelligence Prediction -- Surface-based Brain Morphometry for the Prediction of Fluid Intelligence in the Neurocognitive Prediction Challenge 2019 -- Prediction of Fluid Intelligence From T1-Weighted Magnetic Resonance Images -- Ensemble of SVM, Random-Forest and the BSWiMS Method to Predict and Describe Structural Associations with Fluid Intelligence Scores from T1-Weighed MRI -- Predicting intelligence based on cortical WM/GM contrast, cortical thickness and volumetry -- Predict Fluid Intelligence of Adolescent Using Ensemble Learning -- Predicting Fluid Intelligence in Adolescent Brain MRI Data: An Ensemble Approach -- Predicting Fluid intelligence from structural MRI using Random Forest regression -- Nu Support Vector Machinein Prediction of Fluid Intelligence Using MRI Data -- An AutoML Approach for the Prediction of Fluid Intelligence From MRI-Derived Features -- Predicting Fluid Intelligence from MRI images with Encoder-decoder Regularization -- ABCD Neurocognitive Prediction Challenge 2019: Predicting individual residual fluid intelligence scores from cortical grey matter morphology -- Ensemble Modeling of Neurocognitive Performance Using MRI-derived Brain Structure Volumes -- ABCD Neurocognitive Prediction Challenge 2019: Predicting individual fluid intelligence scores from structural MRI using probabilistic segmentation and kernel ridge regression -- Predicting fluid intelligence using anatomical measures within functionally defined brain networks -- Sex differences in predicting fluid intelligence of adolescent brain from T1-weighted MRIs -- Ensemble of 3D CNN regressors with data fusion for fluid intelligence prediction -- Adolescent fluid intelligence prediction from regional brain volumes and cortical curvatures using BlockPC-XGBoost -- Cortical and Subcortical Contributions to Predicting Intelligence using 3D ConvNets. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Adolescent Brain Cognitive Development Neurocognitive Prediction : First Challenge, ABCD-NP 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings [documento electrónico] / Pohl, Kilian M., ; Thompson, Wesley K., ; Adeli, Ehsan, ; Linguraru, Marius George, . - 1 ed. . - [s.l.] : Springer, 2019 . - XI, 188 p. 57 ilustraciones, 49 ilustraciones en color.
ISBN : 978-3-030-31901-4
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Palabras clave: Visión por computador Aprendizaje automático Informática Estadistica matematica Procesamiento de datos Probabilidad y EstadÃstica en Informática MinerÃa de datos y descubrimiento de conocimientos Clasificación: Resumen: Este libro constituye las actas arbitradas del Primer DesafÃo en Predicción Neurocognitiva del Desarrollo Cognitivo del Cerebro de Adolescentes, ABCD-NP 2019, celebrado junto con MICCAI 2019, en Shenzhen, China, en octubre de 2019. Se revisaron cuidadosamente 29 presentaciones y 24 de ellas fueron aceptadas. . Algunas de las 24 presentaciones se fusionaron y dieron como resultado los 21 artÃculos que se presentan en este libro. Los artÃculos exploran métodos para predecir la inteligencia fluida a partir de resonancia magnética ponderada en T1 de 8669 niños (de 9 a 10 años de edad) reclutados por el estudio Adolescent Brain Cognitive Development Study (ABCD); el mayor estudio a largo plazo sobre el desarrollo del cerebro y la salud infantil en los Estados Unidos hasta la fecha. Nota de contenido: A Combined Deep Learning-Gradient Boosting Machine Framework for Fluid Intelligence Prediction -- Predicting Fluid Intelligence of Children using T1-weighted MR Images and a StackNet -- Deep Learning vs. Classical Machine Learning: A Comparison of Methods for Fluid Intelligence Prediction -- Surface-based Brain Morphometry for the Prediction of Fluid Intelligence in the Neurocognitive Prediction Challenge 2019 -- Prediction of Fluid Intelligence From T1-Weighted Magnetic Resonance Images -- Ensemble of SVM, Random-Forest and the BSWiMS Method to Predict and Describe Structural Associations with Fluid Intelligence Scores from T1-Weighed MRI -- Predicting intelligence based on cortical WM/GM contrast, cortical thickness and volumetry -- Predict Fluid Intelligence of Adolescent Using Ensemble Learning -- Predicting Fluid Intelligence in Adolescent Brain MRI Data: An Ensemble Approach -- Predicting Fluid intelligence from structural MRI using Random Forest regression -- Nu Support Vector Machinein Prediction of Fluid Intelligence Using MRI Data -- An AutoML Approach for the Prediction of Fluid Intelligence From MRI-Derived Features -- Predicting Fluid Intelligence from MRI images with Encoder-decoder Regularization -- ABCD Neurocognitive Prediction Challenge 2019: Predicting individual residual fluid intelligence scores from cortical grey matter morphology -- Ensemble Modeling of Neurocognitive Performance Using MRI-derived Brain Structure Volumes -- ABCD Neurocognitive Prediction Challenge 2019: Predicting individual fluid intelligence scores from structural MRI using probabilistic segmentation and kernel ridge regression -- Predicting fluid intelligence using anatomical measures within functionally defined brain networks -- Sex differences in predicting fluid intelligence of adolescent brain from T1-weighted MRIs -- Ensemble of 3D CNN regressors with data fusion for fluid intelligence prediction -- Adolescent fluid intelligence prediction from regional brain volumes and cortical curvatures using BlockPC-XGBoost -- Cortical and Subcortical Contributions to Predicting Intelligence using 3D ConvNets. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Predictive Intelligence in Medicine : 4th International Workshop, PRIME 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings Tipo de documento: documento electrónico Autores: Rekik, Islem, ; Adeli, Ehsan, ; Park, Sang Hyun, ; Schnabel, Julia, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2021 Número de páginas: XIII, 280 p. 80 ilustraciones, 68 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-87602-9 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Palabras clave: Inteligencia artificial Bioinformática BiologÃa Computacional y de Sistemas IngenierÃa Informática y Redes Red informática IngenierÃa Informática Visión por computador Visión Procesamiento de imágenes Clasificación: Resumen: Este libro constituye las actas del 4.º Taller Internacional sobre Inteligencia Predictiva en Medicina, PRIME 2021, celebrado junto con MICCAI 2021, en Estrasburgo, Francia, en octubre de 2021.* Los 25 artÃculos presentados en este volumen fueron cuidadosamente revisados ​​y seleccionados para su inclusión. en este libro. Las contribuciones describen nuevos modelos y métodos predictivos de vanguardia que resuelven problemas desafiantes en el campo médico para una medicina predictiva de alta precisión. *El taller se realizó de manera virtual. Nota de contenido: Self-Supervised Learning based CT Denoising using Pseudo-CT Image Pairs -- A Few-shot Learning Graph Multi-Trajectory Evolution Network for Forecasting Multimodal Baby Connectivity Development from a Baseline Timepoint -- One Representative-Shot Learning Using a Population-Driven Template with Application to Brain Connectivity Classification and Evolution Prediction -- Mixing-AdaSIN: Constructing a De-biased Dataset using Adaptive Structural Instance Normalization and Texture Mixing -- Liver Tumor Localization and Characterization from Multi-Phase MR Volumes Using Key-Slice Prediction: A Physician-Inspired Approach -- Improving Tuberculosis Recognition on Bone-Suppressed Chest X-rays Guided by Task-Specific Features -- Template-Based Inter-modality Super-resolution of Brain Connectivity -- Adversarial Bayesian Optimization for Quantifying Motion Artifact within MRI -- False Positive Suppression in Cervical Cell Screening via Attention-Guided Semi-Supervised Learning -- Investigating and Quantifying the Reproducibility of Graph Neural Networks in Predictive Medicine -- Self Supervised Contrastive Learning on Multiple Breast Modalities Boosts Classification Performance -- Self-Guided Multi-Attention Network for Periventricular Leukomalacia Recognition -- Opportunistic Screening of Osteoporosis Using Plain Film Chest X-ray -- Multi-Task Deep Segmentation and Radiomics for Automatic Prognosis in Head and Neck Cancer -- Integrating Multimodal MRIs for Adult ADHD Identification with Heterogeneous Graph Attention Convolutional Network -- Probabilistic Deep Learning with Adversarial Training and Volume Interval Estimation – Better Ways to Perform and Evaluate Predictive Models for White Matter Hyperintensities Evolution -- A Multi-scale Capsule Network for Improving Diagnostic Generalizability in Breast Cancer Diagnosis using Ultrasonography -- Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy using Multi-scale Patch Learning with Mammography -- The Pitfalls of SampleSelection: A Case Study on Lung Nodule Classification -- Anatomical Structure-aware Pulmonary Nodule Detection via Parallel Multi-Task RoI Head -- Towards Cancer Patients Classification Using Liquid Biopsy -- Foreseeing Survival through `Fuzzy Intelligence': A cognitively-inspired incremental learning based de novo model for Breast Cancer Prognosis by multi-omics data fusion -- Improving Across Dataset Brain Age Predictions using Transfer Learning -- Uncertainty-Based Dynamic Graph Neighborhoods For Medical Segmentation -- FLAT-Net: Longitudinal Brain Graph Evolution Prediction from a Few Training Representative Templates. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Predictive Intelligence in Medicine : 4th International Workshop, PRIME 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings [documento electrónico] / Rekik, Islem, ; Adeli, Ehsan, ; Park, Sang Hyun, ; Schnabel, Julia, . - 1 ed. . - [s.l.] : Springer, 2021 . - XIII, 280 p. 80 ilustraciones, 68 ilustraciones en color.
ISBN : 978-3-030-87602-9
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Palabras clave: Inteligencia artificial Bioinformática BiologÃa Computacional y de Sistemas IngenierÃa Informática y Redes Red informática IngenierÃa Informática Visión por computador Visión Procesamiento de imágenes Clasificación: Resumen: Este libro constituye las actas del 4.º Taller Internacional sobre Inteligencia Predictiva en Medicina, PRIME 2021, celebrado junto con MICCAI 2021, en Estrasburgo, Francia, en octubre de 2021.* Los 25 artÃculos presentados en este volumen fueron cuidadosamente revisados ​​y seleccionados para su inclusión. en este libro. Las contribuciones describen nuevos modelos y métodos predictivos de vanguardia que resuelven problemas desafiantes en el campo médico para una medicina predictiva de alta precisión. *El taller se realizó de manera virtual. Nota de contenido: Self-Supervised Learning based CT Denoising using Pseudo-CT Image Pairs -- A Few-shot Learning Graph Multi-Trajectory Evolution Network for Forecasting Multimodal Baby Connectivity Development from a Baseline Timepoint -- One Representative-Shot Learning Using a Population-Driven Template with Application to Brain Connectivity Classification and Evolution Prediction -- Mixing-AdaSIN: Constructing a De-biased Dataset using Adaptive Structural Instance Normalization and Texture Mixing -- Liver Tumor Localization and Characterization from Multi-Phase MR Volumes Using Key-Slice Prediction: A Physician-Inspired Approach -- Improving Tuberculosis Recognition on Bone-Suppressed Chest X-rays Guided by Task-Specific Features -- Template-Based Inter-modality Super-resolution of Brain Connectivity -- Adversarial Bayesian Optimization for Quantifying Motion Artifact within MRI -- False Positive Suppression in Cervical Cell Screening via Attention-Guided Semi-Supervised Learning -- Investigating and Quantifying the Reproducibility of Graph Neural Networks in Predictive Medicine -- Self Supervised Contrastive Learning on Multiple Breast Modalities Boosts Classification Performance -- Self-Guided Multi-Attention Network for Periventricular Leukomalacia Recognition -- Opportunistic Screening of Osteoporosis Using Plain Film Chest X-ray -- Multi-Task Deep Segmentation and Radiomics for Automatic Prognosis in Head and Neck Cancer -- Integrating Multimodal MRIs for Adult ADHD Identification with Heterogeneous Graph Attention Convolutional Network -- Probabilistic Deep Learning with Adversarial Training and Volume Interval Estimation – Better Ways to Perform and Evaluate Predictive Models for White Matter Hyperintensities Evolution -- A Multi-scale Capsule Network for Improving Diagnostic Generalizability in Breast Cancer Diagnosis using Ultrasonography -- Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy using Multi-scale Patch Learning with Mammography -- The Pitfalls of SampleSelection: A Case Study on Lung Nodule Classification -- Anatomical Structure-aware Pulmonary Nodule Detection via Parallel Multi-Task RoI Head -- Towards Cancer Patients Classification Using Liquid Biopsy -- Foreseeing Survival through `Fuzzy Intelligence': A cognitively-inspired incremental learning based de novo model for Breast Cancer Prognosis by multi-omics data fusion -- Improving Across Dataset Brain Age Predictions using Transfer Learning -- Uncertainty-Based Dynamic Graph Neighborhoods For Medical Segmentation -- FLAT-Net: Longitudinal Brain Graph Evolution Prediction from a Few Training Representative Templates. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : PRedictive Intelligence in MEdicine : First International Workshop, PRIME 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings Tipo de documento: documento electrónico Autores: Rekik, Islem, ; Unal, Gozde, ; Adeli, Ehsan, ; Park, Sang Hyun, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2018 Número de páginas: XII, 174 p. 72 ilustraciones ISBN/ISSN/DL: 978-3-030-00320-3 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Palabras clave: Visión por computador Redes de comunicación informática IngenierÃa Informática y Redes Reconocimiento de patrones automatizado Inteligencia artificial Red informática Sistemas de reconocimiento de patrones IngenierÃa Informática Clasificación: Resumen: Este libro constituye las actas arbitradas del Primer Taller Internacional sobre Inteligencia Predictiva en Medicina, PRIME 2018, celebrado junto con MICCAI 2018, en Granada, España, en septiembre de 2018. Los 20 artÃculos completos presentados fueron cuidadosamente revisados ​​y seleccionados entre 23 presentaciones. El principal objetivo del taller es impulsar la aparición de modelos predictivos en un sentido amplio, con aplicación a datos médicos. En particular, el taller admitirá artÃculos que describan nuevos modelos y métodos predictivos de vanguardia que resuelven problemas desafiantes en el campo médico. Nota de contenido: Computer Aided Identification of Motion Disturbances Related to Parkinson's Disease -- Prediction of Severity and Treatment Outcome for ASD from fMRI -- Enhancement of Perivascular Spaces Using a Very Deep 3D Dense Network -- Generation of Amyloid PET Images via Conditional Adversarial Training for Predicting Progression to Alzheimer's Disease -- Prediction of Hearing Loss Based on Auditory Perception: A Preliminary Study -- Predictive Patient Care: Survival Model to Prevent Medication Non-adherence -- Joint Robust Imputation and Classification for Early Dementia Detection Using Incomplete Multi-Modality Data -- Shared Latent Structures Between Imaging Features and Biomarkers in Early Stages of Alzheimer's Disease -- Predicting Nucleus Basalis of Meynert Volume from Compartmental Brain Segmentations -- Multi-modal Neuroimaging Data Fusion via Latent Space Learning for Alzheimer's Disease Diagnosis -- Transfer Learning for Task Adaptation of Brain Lesion Assessment and Prediction of Brain Abnormalities Progression/Regression Using Irregularity Age Map in Brain MRI -- Multi-View Brain Network Prediction From a Source View Using Sample Selection via CCA-based Multi-Kernel Connectomic Manifold Learning -- Predicting Emotional Intelligence Scores From Multi-Session Functional Brain Connectomes -- Predictive Modeling of Longitudinal Data for Alzheimer's Disease Diagnosis Using RNNs -- Towards Continuous Health Diagnosis from Faces with Deep Learning -- XmoNet: A Fully Convolutional Network for Cross-Modality MR Image Inference -- 3D Convolutional Neural Network and Stacked Bidirectional Recurrent Neural Network for Alzheimer's Disease Diagnosis -- Generative Adversarial Training for MRA Image Synthesis Using Multi-Contrast MRI -- Diffusion MRI Spatial Super-Resolution Using Generative Adversarialv Networks -- Prediction to Atrial Fibrillation Using Deep Convolutional Neural Networks. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] PRedictive Intelligence in MEdicine : First International Workshop, PRIME 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings [documento electrónico] / Rekik, Islem, ; Unal, Gozde, ; Adeli, Ehsan, ; Park, Sang Hyun, . - 1 ed. . - [s.l.] : Springer, 2018 . - XII, 174 p. 72 ilustraciones.
ISBN : 978-3-030-00320-3
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Palabras clave: Visión por computador Redes de comunicación informática IngenierÃa Informática y Redes Reconocimiento de patrones automatizado Inteligencia artificial Red informática Sistemas de reconocimiento de patrones IngenierÃa Informática Clasificación: Resumen: Este libro constituye las actas arbitradas del Primer Taller Internacional sobre Inteligencia Predictiva en Medicina, PRIME 2018, celebrado junto con MICCAI 2018, en Granada, España, en septiembre de 2018. Los 20 artÃculos completos presentados fueron cuidadosamente revisados ​​y seleccionados entre 23 presentaciones. El principal objetivo del taller es impulsar la aparición de modelos predictivos en un sentido amplio, con aplicación a datos médicos. En particular, el taller admitirá artÃculos que describan nuevos modelos y métodos predictivos de vanguardia que resuelven problemas desafiantes en el campo médico. Nota de contenido: Computer Aided Identification of Motion Disturbances Related to Parkinson's Disease -- Prediction of Severity and Treatment Outcome for ASD from fMRI -- Enhancement of Perivascular Spaces Using a Very Deep 3D Dense Network -- Generation of Amyloid PET Images via Conditional Adversarial Training for Predicting Progression to Alzheimer's Disease -- Prediction of Hearing Loss Based on Auditory Perception: A Preliminary Study -- Predictive Patient Care: Survival Model to Prevent Medication Non-adherence -- Joint Robust Imputation and Classification for Early Dementia Detection Using Incomplete Multi-Modality Data -- Shared Latent Structures Between Imaging Features and Biomarkers in Early Stages of Alzheimer's Disease -- Predicting Nucleus Basalis of Meynert Volume from Compartmental Brain Segmentations -- Multi-modal Neuroimaging Data Fusion via Latent Space Learning for Alzheimer's Disease Diagnosis -- Transfer Learning for Task Adaptation of Brain Lesion Assessment and Prediction of Brain Abnormalities Progression/Regression Using Irregularity Age Map in Brain MRI -- Multi-View Brain Network Prediction From a Source View Using Sample Selection via CCA-based Multi-Kernel Connectomic Manifold Learning -- Predicting Emotional Intelligence Scores From Multi-Session Functional Brain Connectomes -- Predictive Modeling of Longitudinal Data for Alzheimer's Disease Diagnosis Using RNNs -- Towards Continuous Health Diagnosis from Faces with Deep Learning -- XmoNet: A Fully Convolutional Network for Cross-Modality MR Image Inference -- 3D Convolutional Neural Network and Stacked Bidirectional Recurrent Neural Network for Alzheimer's Disease Diagnosis -- Generative Adversarial Training for MRA Image Synthesis Using Multi-Contrast MRI -- Diffusion MRI Spatial Super-Resolution Using Generative Adversarialv Networks -- Prediction to Atrial Fibrillation Using Deep Convolutional Neural Networks. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Predictive Intelligence in Medicine : Second International Workshop, PRIME 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings Tipo de documento: documento electrónico Autores: Rekik, Islem, ; Adeli, Ehsan, ; Park, Sang Hyun, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2019 Número de páginas: XIII, 178 p. 58 ilustraciones, 48 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-32281-6 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Palabras clave: Visión Probabilidad y EstadÃstica en Informática Inteligencia artificial Procesamiento de datos Algoritmo Procesamiento de imágenes Matemáticas Informática MinerÃa de datos y descubrimiento de conocimientos Visión por computador Clasificación: Resumen: Este libro constituye las actas del Segundo Taller Internacional sobre Inteligencia Predictiva en Medicina, PRIME 2019, celebrado junto con MICCAI 2019, en Shenzhen, China, en octubre de 2019. Los 18 artÃculos presentados en este volumen fueron cuidadosamente revisados ​​y seleccionados para su inclusión en este libro. Las contribuciones describen nuevos modelos y métodos predictivos de vanguardia que resuelven problemas desafiantes en el campo médico para una medicina predictiva de alta precisión. . Nota de contenido: TADPOLE Challenge: Accurate Alzheimer's disease prediction through crowdsourced forecasting of future data -- Inter-fractional Respiratory Motion Modelling from Abdominal Ultrasound: A Feasibility Study -- Adaptive Neuro-Fuzzy Inference System-based Chaotic Swarm Intelligence Hybrid Model for Recognition of Mild Cognitive Impairment from Resting-state fMRI -- Deep Learning via Fused Bidirectional Attention Stacked Long Short-term Memory for Obsessive-Compulsive Disorder Diagnosis and Risk Screening -- Modeling Disease Progression In Retinal OCTs With Longitudinal Self-Supervised Learning -- Predicting Response to the Antidepressant Bupropion using Pretreatment fMRI -- Progressive Infant Brain Connectivity Evolution Prediction from Neonatal MRI using Bidirectionally Supervised Sample Selection -- Computed Tomography Image-Based Deep Survival Regression for Metastatic Colorectal Cancer using a Non-Proportional Hazards Model -- 7 years of Developing Seed Techniques for Alzheimer's Disease Diagnosis using Brain Image and Connectivity Data Largely Bypassed Prediction for Prognosis -- Generative Adversarial Irregularity Detection in Mammography Images -- Hierarchical Adversarial Connectomic Domain Alignment for Target Brain Graph Prediction and Classification From a Source Graph -- Predicting High-Resolution Brain Networks Using Hierarchically Embedded and Aligned Multi-Resolution Neighborhoods -- Catheter Synthesis in X-Ray Fluoroscopy with Generative Adversarial Networks -- Prediction of Clinical Scores for Subjective Cognitive Decline and Mild Cognitive Impairment -- Diagnosis of Parkinsons Disease in Genetic Cohort Patients via Stage-wise Hierarchical Deep Polynomial Ensemble learning -- Automatic Detection of Bowel Disease with Residual Networks -- Support Vector based Autoregressive Mixed Models of Longitudinal Brain Changes and Corresponding Genetics in Alzheimers Disease -- Treatment Response Prediction of Hepatocellular Carcinoma Patients from Abdominal CT Images with Deep Convolutional Neural Networks. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Predictive Intelligence in Medicine : Second International Workshop, PRIME 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings [documento electrónico] / Rekik, Islem, ; Adeli, Ehsan, ; Park, Sang Hyun, . - 1 ed. . - [s.l.] : Springer, 2019 . - XIII, 178 p. 58 ilustraciones, 48 ilustraciones en color.
ISBN : 978-3-030-32281-6
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Palabras clave: Visión Probabilidad y EstadÃstica en Informática Inteligencia artificial Procesamiento de datos Algoritmo Procesamiento de imágenes Matemáticas Informática MinerÃa de datos y descubrimiento de conocimientos Visión por computador Clasificación: Resumen: Este libro constituye las actas del Segundo Taller Internacional sobre Inteligencia Predictiva en Medicina, PRIME 2019, celebrado junto con MICCAI 2019, en Shenzhen, China, en octubre de 2019. Los 18 artÃculos presentados en este volumen fueron cuidadosamente revisados ​​y seleccionados para su inclusión en este libro. Las contribuciones describen nuevos modelos y métodos predictivos de vanguardia que resuelven problemas desafiantes en el campo médico para una medicina predictiva de alta precisión. . Nota de contenido: TADPOLE Challenge: Accurate Alzheimer's disease prediction through crowdsourced forecasting of future data -- Inter-fractional Respiratory Motion Modelling from Abdominal Ultrasound: A Feasibility Study -- Adaptive Neuro-Fuzzy Inference System-based Chaotic Swarm Intelligence Hybrid Model for Recognition of Mild Cognitive Impairment from Resting-state fMRI -- Deep Learning via Fused Bidirectional Attention Stacked Long Short-term Memory for Obsessive-Compulsive Disorder Diagnosis and Risk Screening -- Modeling Disease Progression In Retinal OCTs With Longitudinal Self-Supervised Learning -- Predicting Response to the Antidepressant Bupropion using Pretreatment fMRI -- Progressive Infant Brain Connectivity Evolution Prediction from Neonatal MRI using Bidirectionally Supervised Sample Selection -- Computed Tomography Image-Based Deep Survival Regression for Metastatic Colorectal Cancer using a Non-Proportional Hazards Model -- 7 years of Developing Seed Techniques for Alzheimer's Disease Diagnosis using Brain Image and Connectivity Data Largely Bypassed Prediction for Prognosis -- Generative Adversarial Irregularity Detection in Mammography Images -- Hierarchical Adversarial Connectomic Domain Alignment for Target Brain Graph Prediction and Classification From a Source Graph -- Predicting High-Resolution Brain Networks Using Hierarchically Embedded and Aligned Multi-Resolution Neighborhoods -- Catheter Synthesis in X-Ray Fluoroscopy with Generative Adversarial Networks -- Prediction of Clinical Scores for Subjective Cognitive Decline and Mild Cognitive Impairment -- Diagnosis of Parkinsons Disease in Genetic Cohort Patients via Stage-wise Hierarchical Deep Polynomial Ensemble learning -- Automatic Detection of Bowel Disease with Residual Networks -- Support Vector based Autoregressive Mixed Models of Longitudinal Brain Changes and Corresponding Genetics in Alzheimers Disease -- Treatment Response Prediction of Hepatocellular Carcinoma Patients from Abdominal CT Images with Deep Convolutional Neural Networks. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Predictive Intelligence in Medicine : Third International Workshop, PRIME 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings Tipo de documento: documento electrónico Autores: Rekik, Islem, ; Adeli, Ehsan, ; Park, Sang Hyun, ; Valdés Hernández, Maria del C., Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2020 Número de páginas: XII, 212 p. 65 ilustraciones ISBN/ISSN/DL: 978-3-030-59354-4 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Palabras clave: IngenierÃa Informática y Redes Visión Entornos informáticos Red informática Inteligencia artificial Visión por computador Procesamiento de imágenes Ordenador IngenierÃa Informática Aplicaciones informáticas y de sistemas de información Clasificación: Resumen: Este libro constituye las actas del Tercer Taller Internacional sobre Inteligencia Predictiva en Medicina, PRIME 2020, realizado en conjunto con MICCAI 2020, en Lima, Perú, en octubre de 2020. El taller se llevó a cabo de manera virtual debido a la pandemia de COVID-19. Los 17 artÃculos completos y 2 artÃculos breves presentados en este volumen fueron cuidadosamente revisados ​​y seleccionados para su inclusión en este libro. Las contribuciones describen nuevos modelos y métodos predictivos de vanguardia que resuelven problemas desafiantes en el campo médico para una medicina predictiva de alta precisión. Nota de contenido: Context-Aware Synergetic Multiplex Network for Multi-Organ Segmentation of Cervical Cancer MRI -- Residual Embedding Similarity-Based Network Selection for Predicting Brain Network Evolution Trajectory from a Single Observation -- Adversarial Brain Multiplex Prediction From a Single Network for High-Order Connectional Gender-Specific Brain Mapping -- Learned deep radiomics for survival analysis with attention -- Robustification of Segmentation Models Against Adversarial Perturbations In Medical Imaging -- Joint Clinical Data and CT Image based Prognosis: A Case Study on Postoperative Pulmonary Venous Obstruction Prediction -- Low-Dose CT Denoising using Octave Convolution with High and Low Frequency bands -- Conditional Generative Adversarial Network for Predicting 3D Medical Images Affected by Alzheimer's Diseases -- Inpainting Cropped Diffusion MRI using Deep Generative Models -- Multi-View Brain HyperConnectome AutoEncoder For Brain State Classification -- Foreseeing Brain Graph EvolutionOver Time Using Deep Adversarial Network Normalizer -- Longitudinal prediction of anatomical changes of parotid glands -- Deep Parametric Mixtures for Modeling the Functional Connectome -- Deep EvoGraphNet Architecture For Time-Dependent Brain Graph Data Synthesis From a Single Timepoint -- Uniformizing Techniques to Process CT scans with 3D CNNs for Tuberculosis Prediction -- mr2 NST: Multi-Resolution and Multi-Reference Neural Style Transfer for Mammography -- Template-oriented Multi-task Sparse Low-rank Learning for Parkinson's Diseases Diagnosis -- Multimodal Prediction of Breast Cancer Relapse Prior to Neoadjuvant Chemotherapy Treatment. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Predictive Intelligence in Medicine : Third International Workshop, PRIME 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings [documento electrónico] / Rekik, Islem, ; Adeli, Ehsan, ; Park, Sang Hyun, ; Valdés Hernández, Maria del C., . - 1 ed. . - [s.l.] : Springer, 2020 . - XII, 212 p. 65 ilustraciones.
ISBN : 978-3-030-59354-4
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Palabras clave: IngenierÃa Informática y Redes Visión Entornos informáticos Red informática Inteligencia artificial Visión por computador Procesamiento de imágenes Ordenador IngenierÃa Informática Aplicaciones informáticas y de sistemas de información Clasificación: Resumen: Este libro constituye las actas del Tercer Taller Internacional sobre Inteligencia Predictiva en Medicina, PRIME 2020, realizado en conjunto con MICCAI 2020, en Lima, Perú, en octubre de 2020. El taller se llevó a cabo de manera virtual debido a la pandemia de COVID-19. Los 17 artÃculos completos y 2 artÃculos breves presentados en este volumen fueron cuidadosamente revisados ​​y seleccionados para su inclusión en este libro. Las contribuciones describen nuevos modelos y métodos predictivos de vanguardia que resuelven problemas desafiantes en el campo médico para una medicina predictiva de alta precisión. Nota de contenido: Context-Aware Synergetic Multiplex Network for Multi-Organ Segmentation of Cervical Cancer MRI -- Residual Embedding Similarity-Based Network Selection for Predicting Brain Network Evolution Trajectory from a Single Observation -- Adversarial Brain Multiplex Prediction From a Single Network for High-Order Connectional Gender-Specific Brain Mapping -- Learned deep radiomics for survival analysis with attention -- Robustification of Segmentation Models Against Adversarial Perturbations In Medical Imaging -- Joint Clinical Data and CT Image based Prognosis: A Case Study on Postoperative Pulmonary Venous Obstruction Prediction -- Low-Dose CT Denoising using Octave Convolution with High and Low Frequency bands -- Conditional Generative Adversarial Network for Predicting 3D Medical Images Affected by Alzheimer's Diseases -- Inpainting Cropped Diffusion MRI using Deep Generative Models -- Multi-View Brain HyperConnectome AutoEncoder For Brain State Classification -- Foreseeing Brain Graph EvolutionOver Time Using Deep Adversarial Network Normalizer -- Longitudinal prediction of anatomical changes of parotid glands -- Deep Parametric Mixtures for Modeling the Functional Connectome -- Deep EvoGraphNet Architecture For Time-Dependent Brain Graph Data Synthesis From a Single Timepoint -- Uniformizing Techniques to Process CT scans with 3D CNNs for Tuberculosis Prediction -- mr2 NST: Multi-Resolution and Multi-Reference Neural Style Transfer for Mammography -- Template-oriented Multi-task Sparse Low-rank Learning for Parkinson's Diseases Diagnosis -- Multimodal Prediction of Breast Cancer Relapse Prior to Neoadjuvant Chemotherapy Treatment. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
