Información del autor
Autor Rueckert, Daniel |
Documentos disponibles escritos por este autor (3)
Crear una solicitud de compra Refinar búsquedaClinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning / Oyarzun Laura, Cristina ; Cardoso, M. Jorge ; Rosen-Zvi, Michal ; Kaissis, Georgios ; Linguraru, Marius George ; Shekhar, Raj ; Wesarg, Stefan ; Erdt, Marius ; Drechsler, Klaus ; Chen, Yufei ; Albarqouni, Shadi ; Bakas, Spyridon ; Landman, Bennett ; Rieke, Nicola ; Roth, Holger ; Li, Xiaoxiao ; Xu, Daguang ; Gabrani, Maria ; Konukoglu, Ender ; Guindy, Michal ; Rueckert, Daniel ; Ziller, Alexander ; Usynin, Dmitrii ; Passerat-Palmbach, Jonathan
TÃtulo : 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 Tipo de documento: documento electrónico Autores: Oyarzun Laura, Cristina, ; Cardoso, M. Jorge, ; Rosen-Zvi, Michal, ; Kaissis, Georgios, ; Linguraru, Marius George, ; Shekhar, Raj, ; Wesarg, Stefan, ; Erdt, Marius, ; Drechsler, Klaus, ; Chen, Yufei, ; Albarqouni, Shadi, ; Bakas, Spyridon, ; Landman, Bennett, ; Rieke, Nicola, ; Roth, Holger, ; Li, Xiaoxiao, ; Xu, Daguang, ; Gabrani, Maria, ; Konukoglu, Ender, ; Guindy, Michal, ; Rueckert, Daniel, ; Ziller, Alexander, ; Usynin, Dmitrii, ; Passerat-Palmbach, Jonathan, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2021 Número de páginas: XXV, 190 p. 78 ilustraciones, 67 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-90874-4 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Visión por computador Aprendizaje automático Red de computadoras Ciencias sociales Redes de comunicación informática Aplicación informática en ciencias sociales y del comportamiento. Clasificación: 006.37 Resumen: Este libro constituye las actas arbitradas del décimo taller internacional sobre procedimientos clÃnicos basados ​​en imágenes, CLIP 2021, segundo taller MICCAI sobre aprendizaje distribuido y colaborativo, DCL 2021, primer taller MICCAI, LL-COVID19, primer aprendizaje automático seguro y que preserva la privacidad para Taller y tutorial sobre imágenes médicas, PPML 2021, celebrado junto con MICCAI 2021, en octubre de 2021. Estaba previsto que los talleres se llevaran a cabo en Estrasburgo, Francia, pero se llevaron a cabo virtualmente debido a la pandemia de COVID-19. CLIP 2021 aceptó 9 artÃculos de las 13 presentaciones recibidas. Se centra en modelos holÃsticos de pacientes para una atención sanitaria personalizada con el objetivo de acercar los métodos de investigación básica a la práctica clÃnica. Para DCL 2021, se aceptó para publicación 4 artÃculos de 7 presentaciones. Se ocupan del aprendizaje automático aplicado a problemas en los que los datos no se pueden almacenar en bases de datos centralizadas y la privacidad de la información es una prioridad. LL-COVID19 2021 aceptó 2 artÃculos de 3 presentados que trataban sobre el uso de modelos de IA en la práctica clÃnica. Y para PPML 2021, se aceptaron 2 artÃculos de un total de 6 presentaciones, que exploraban el uso de técnicas de privacidad en la comunidad de imágenes médicas. Nota de contenido: Intestine segmentation with small computational cost for diagnosis assistance of ileus and intestinal obstruction -- Generation of Patient-Specific, Ligamentoskeletal, Finite Element Meshes for Scoliosis Correction Planning -- Bayesian Graph Neural Networks For EEG-based Emotion Recognition -- ViTBIS: Vision Transformer for Biomedical Image Segmentation -- Attention-guided pancreatic duct segmentation from abdominal CT volumes -- Development of the Next Generation Hand-Held Doppler With Waveform Phasicity Predictive Capabilities Using Deep Learning -- Learning from mistakes: an error-driven mechanism to improve segmentation performance based on expert feedback -- TMJOAI: an artificial web-based intelligence tool for early diagnosis of the Temporomandibular Joint Osteoarthritis -- COVID-19 Infection Segmentation from Chest CT Images Based on Scale Uncertainty -- Multi-task Federated Learning for Heterogeneous Pancreas Segmentation -- Federated Learning in the Cloud for Analysis of Medical Images- Experience with Open Source Frameworks -- On the Fairness of Swarm Learning in Skin Lesion Classification -- Lessons learned from the development and application of medical imaging-based AI technologies for combating COVID-19: why discuss, what next -- The Role of Pleura and Adipose in Lung Ultrasound AI -- DuCN: Dual-children Network for Medical Diagnosis and Similar Case Recommendation towards COVID-19 -- Data imputation and reconstruction of distributed Parkinson's disease clinical assessments: A comparative evaluation of two aggregation algorithms -- Defending Medical Image Diagnostics against Privacy Attacks using Generative Methods: Application to Retinal Diagnostics. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the 10th International Workshop on Clinical Image-Based Procedures, CLIP 2021, Second MICCAI Workshop on Distributed and Collaborative Learning, DCL 2021, First MICCAI Workshop, LL-COVID19, First Secure and Privacy-Preserving Machine Learning for Medical Imaging Workshop and Tutorial, PPML 2021, held in conjunction with MICCAI 2021, in October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic. CLIP 2021 accepted 9 papers from the 13 submissions received. It focuses on holistic patient models for personalized healthcare with the goal to bring basic research methods closer to the clinical practice. For DCL 2021, 4 papers from 7 submissions were accepted for publication. They deal with machine learning applied to problems where data cannot be stored in centralized databases and information privacy is a priority. LL-COVID19 2021 accepted 2 papers out of 3 submissions dealing with the use of AI models in clinical practice. And for PPML 2021, 2 papers were accepted from a total of 6 submissions, exploring the use of privacy techniques in the medical imaging community. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] 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 [documento electrónico] / Oyarzun Laura, Cristina, ; Cardoso, M. Jorge, ; Rosen-Zvi, Michal, ; Kaissis, Georgios, ; Linguraru, Marius George, ; Shekhar, Raj, ; Wesarg, Stefan, ; Erdt, Marius, ; Drechsler, Klaus, ; Chen, Yufei, ; Albarqouni, Shadi, ; Bakas, Spyridon, ; Landman, Bennett, ; Rieke, Nicola, ; Roth, Holger, ; Li, Xiaoxiao, ; Xu, Daguang, ; Gabrani, Maria, ; Konukoglu, Ender, ; Guindy, Michal, ; Rueckert, Daniel, ; Ziller, Alexander, ; Usynin, Dmitrii, ; Passerat-Palmbach, Jonathan, . - 1 ed. . - [s.l.] : Springer, 2021 . - XXV, 190 p. 78 ilustraciones, 67 ilustraciones en color.
ISBN : 978-3-030-90874-4
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Idioma : Inglés (eng)
Palabras clave: Visión por computador Aprendizaje automático Red de computadoras Ciencias sociales Redes de comunicación informática Aplicación informática en ciencias sociales y del comportamiento. Clasificación: 006.37 Resumen: Este libro constituye las actas arbitradas del décimo taller internacional sobre procedimientos clÃnicos basados ​​en imágenes, CLIP 2021, segundo taller MICCAI sobre aprendizaje distribuido y colaborativo, DCL 2021, primer taller MICCAI, LL-COVID19, primer aprendizaje automático seguro y que preserva la privacidad para Taller y tutorial sobre imágenes médicas, PPML 2021, celebrado junto con MICCAI 2021, en octubre de 2021. Estaba previsto que los talleres se llevaran a cabo en Estrasburgo, Francia, pero se llevaron a cabo virtualmente debido a la pandemia de COVID-19. CLIP 2021 aceptó 9 artÃculos de las 13 presentaciones recibidas. Se centra en modelos holÃsticos de pacientes para una atención sanitaria personalizada con el objetivo de acercar los métodos de investigación básica a la práctica clÃnica. Para DCL 2021, se aceptó para publicación 4 artÃculos de 7 presentaciones. Se ocupan del aprendizaje automático aplicado a problemas en los que los datos no se pueden almacenar en bases de datos centralizadas y la privacidad de la información es una prioridad. LL-COVID19 2021 aceptó 2 artÃculos de 3 presentados que trataban sobre el uso de modelos de IA en la práctica clÃnica. Y para PPML 2021, se aceptaron 2 artÃculos de un total de 6 presentaciones, que exploraban el uso de técnicas de privacidad en la comunidad de imágenes médicas. Nota de contenido: Intestine segmentation with small computational cost for diagnosis assistance of ileus and intestinal obstruction -- Generation of Patient-Specific, Ligamentoskeletal, Finite Element Meshes for Scoliosis Correction Planning -- Bayesian Graph Neural Networks For EEG-based Emotion Recognition -- ViTBIS: Vision Transformer for Biomedical Image Segmentation -- Attention-guided pancreatic duct segmentation from abdominal CT volumes -- Development of the Next Generation Hand-Held Doppler With Waveform Phasicity Predictive Capabilities Using Deep Learning -- Learning from mistakes: an error-driven mechanism to improve segmentation performance based on expert feedback -- TMJOAI: an artificial web-based intelligence tool for early diagnosis of the Temporomandibular Joint Osteoarthritis -- COVID-19 Infection Segmentation from Chest CT Images Based on Scale Uncertainty -- Multi-task Federated Learning for Heterogeneous Pancreas Segmentation -- Federated Learning in the Cloud for Analysis of Medical Images- Experience with Open Source Frameworks -- On the Fairness of Swarm Learning in Skin Lesion Classification -- Lessons learned from the development and application of medical imaging-based AI technologies for combating COVID-19: why discuss, what next -- The Role of Pleura and Adipose in Lung Ultrasound AI -- DuCN: Dual-children Network for Medical Diagnosis and Similar Case Recommendation towards COVID-19 -- Data imputation and reconstruction of distributed Parkinson's disease clinical assessments: A comparative evaluation of two aggregation algorithms -- Defending Medical Image Diagnostics against Privacy Attacks using Generative Methods: Application to Retinal Diagnostics. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the 10th International Workshop on Clinical Image-Based Procedures, CLIP 2021, Second MICCAI Workshop on Distributed and Collaborative Learning, DCL 2021, First MICCAI Workshop, LL-COVID19, First Secure and Privacy-Preserving Machine Learning for Medical Imaging Workshop and Tutorial, PPML 2021, held in conjunction with MICCAI 2021, in October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic. CLIP 2021 accepted 9 papers from the 13 submissions received. It focuses on holistic patient models for personalized healthcare with the goal to bring basic research methods closer to the clinical practice. For DCL 2021, 4 papers from 7 submissions were accepted for publication. They deal with machine learning applied to problems where data cannot be stored in centralized databases and information privacy is a priority. LL-COVID19 2021 accepted 2 papers out of 3 submissions dealing with the use of AI models in clinical practice. And for PPML 2021, 2 papers were accepted from a total of 6 submissions, exploring the use of privacy techniques in the medical imaging community. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Machine Learning for Medical Image Reconstruction : First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings Tipo de documento: documento electrónico Autores: Knoll, Florian, ; Maier, Andreas, ; Rueckert, Daniel, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2018 Número de páginas: X, 158 p. 67 ilustraciones ISBN/ISSN/DL: 978-3-030-00129-2 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Inteligencia artificial Visión por computador Red de computadoras diseño lógico Informática Médica Redes de comunicación informática Informática de la Salud Clasificación: 006.3 Resumen: Este libro constituye las actas arbitradas del Primer Taller Internacional sobre Aprendizaje Automático para Reconstrucción Médica, MLMIR 2018, celebrado en conjunto con MICCAI 2018, en Granada, España, en septiembre de 2018. Los 17 artÃculos completos presentados fueron cuidadosamente revisados ​​y seleccionados de 21 presentaciones. Los artÃculos están organizados en las siguientes secciones temáticas: aprendizaje profundo para imágenes por resonancia magnética; aprendizaje profundo para tomografÃa computarizada y aprendizaje profundo para reconstrucción general de imágenes. Nota de contenido: Deep learning for magnetic resonance imaging -- Deep learning for computed tomography -- Deep learning for general image reconstruction. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the First International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018. The 17 full papers presented were carefully reviewed and selected from 21 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging; deep learning for computed tomography, and deep learning for general image reconstruction. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Machine Learning for Medical Image Reconstruction : First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings [documento electrónico] / Knoll, Florian, ; Maier, Andreas, ; Rueckert, Daniel, . - 1 ed. . - [s.l.] : Springer, 2018 . - X, 158 p. 67 ilustraciones.
ISBN : 978-3-030-00129-2
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Idioma : Inglés (eng)
Palabras clave: Inteligencia artificial Visión por computador Red de computadoras diseño lógico Informática Médica Redes de comunicación informática Informática de la Salud Clasificación: 006.3 Resumen: Este libro constituye las actas arbitradas del Primer Taller Internacional sobre Aprendizaje Automático para Reconstrucción Médica, MLMIR 2018, celebrado en conjunto con MICCAI 2018, en Granada, España, en septiembre de 2018. Los 17 artÃculos completos presentados fueron cuidadosamente revisados ​​y seleccionados de 21 presentaciones. Los artÃculos están organizados en las siguientes secciones temáticas: aprendizaje profundo para imágenes por resonancia magnética; aprendizaje profundo para tomografÃa computarizada y aprendizaje profundo para reconstrucción general de imágenes. Nota de contenido: Deep learning for magnetic resonance imaging -- Deep learning for computed tomography -- Deep learning for general image reconstruction. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the First International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018. The 17 full papers presented were carefully reviewed and selected from 21 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging; deep learning for computed tomography, and deep learning for general image reconstruction. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Machine Learning for Medical Image Reconstruction : Second International Workshop, MLMIR 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings Tipo de documento: documento electrónico Autores: Knoll, Florian, ; Maier, Andreas, ; Rueckert, Daniel, ; Ye, Jong Chul, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2019 Número de páginas: IX, 266 p. 128 ilustraciones, 94 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-33843-5 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Inteligencia artificial Ciencias sociales Bioinformática Visión por computador Informática Médica Computadoras y Educación Aplicación informática en ciencias sociales y del comportamiento. BiologÃa Computacional y de Sistemas Informática de la Salud Clasificación: 006.3 Resumen: Este libro constituye las actas arbitradas del Segundo Taller Internacional sobre Aprendizaje Automático para la Reconstrucción Médica, MLMIR 2019, celebrado junto con MICCAI 2019, en Shenzhen, China, en octubre de 2019. Los 24 artÃculos completos presentados fueron cuidadosamente revisados ​​y seleccionados entre 32 presentaciones. . Los artÃculos están organizados en las siguientes secciones temáticas: aprendizaje profundo para imágenes por resonancia magnética; aprendizaje profundo para tomografÃa computarizada; y aprendizaje profundo para la reconstrucción general de imágenes. Nota de contenido: Deep Learning for Magnetic Resonance Imaging -- Recon-GLGAN: A Global-Local context based Generative Adversarial Network for MRI Reconstruction- Self-supervised Recurrent Neural Network for 4D Abdominal and In-utero MR Imaging -- Fast Dynamic Perfusion and Angiography Reconstruction using an end-to-end 3D Convolutional Neural Network -- APIR-Net: Autocalibrated Parallel Imaging Reconstruction using a Neural Network -- Accelerated MRI Reconstruction with Dual-domain Generative Adversarial Network -- Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator -- Joint Multi-Anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions -- Modeling and Analysis Brain Development via Discriminative Dictionary Learning -- Deep Learning for Computed Tomography -- Virtual Thin Slice: 3D Conditional GAN-based Super-resolution for CT Slice Interval -- Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior -- Measuring CT Reconstruction Quality with Deep Convolutional Neural Networks -- Deep Learning based Metal Inpainting in the Projection Domain: Initial Results -- Deep Learning for General Image Reconstruction -- Flexible Conditional Image Generation of Missing Data with Learned Mental Maps -- Spatiotemporal PET reconstruction using ML-EM with learned diffeomorphic deformation -- Stain Style Transfer using Transitive Adversarial Networks -- Blind Deconvolution Microscopy Using Cycle Consistent CNN with Explicit PSF Layer -- Deep Learning based approach to quantification of PET tracer uptake in small tumors -- Task-GAN: Improving Generative Adversarial Network for Image Reconstruction -- Gamma Source Location Learning from Synthetic Multi-Pinhole Collimator Data -- Neural Denoising of Ultra-Low Dose Mammography -- Image Reconstruction in a Manifold of Image Patches: Application to Whole-fetus Ultrasound Imaging -- Image Super Resolution via Bilinear Pooling: Application to Confocal Endomicroscopy -- TPSDicyc: Improved Deformation Invariant Cross-domain Medical Image Synthesis -- PredictUS: A Method to Extend the Resolution-Precision Trade-off in Quantitative Ultrasound Image Reconstruction. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the Second International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. The 24 full papers presented were carefully reviewed and selected from 32 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging; deep learning for computed tomography; and deep learning for general image reconstruction. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Machine Learning for Medical Image Reconstruction : Second International Workshop, MLMIR 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings [documento electrónico] / Knoll, Florian, ; Maier, Andreas, ; Rueckert, Daniel, ; Ye, Jong Chul, . - 1 ed. . - [s.l.] : Springer, 2019 . - IX, 266 p. 128 ilustraciones, 94 ilustraciones en color.
ISBN : 978-3-030-33843-5
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Idioma : Inglés (eng)
Palabras clave: Inteligencia artificial Ciencias sociales Bioinformática Visión por computador Informática Médica Computadoras y Educación Aplicación informática en ciencias sociales y del comportamiento. BiologÃa Computacional y de Sistemas Informática de la Salud Clasificación: 006.3 Resumen: Este libro constituye las actas arbitradas del Segundo Taller Internacional sobre Aprendizaje Automático para la Reconstrucción Médica, MLMIR 2019, celebrado junto con MICCAI 2019, en Shenzhen, China, en octubre de 2019. Los 24 artÃculos completos presentados fueron cuidadosamente revisados ​​y seleccionados entre 32 presentaciones. . Los artÃculos están organizados en las siguientes secciones temáticas: aprendizaje profundo para imágenes por resonancia magnética; aprendizaje profundo para tomografÃa computarizada; y aprendizaje profundo para la reconstrucción general de imágenes. Nota de contenido: Deep Learning for Magnetic Resonance Imaging -- Recon-GLGAN: A Global-Local context based Generative Adversarial Network for MRI Reconstruction- Self-supervised Recurrent Neural Network for 4D Abdominal and In-utero MR Imaging -- Fast Dynamic Perfusion and Angiography Reconstruction using an end-to-end 3D Convolutional Neural Network -- APIR-Net: Autocalibrated Parallel Imaging Reconstruction using a Neural Network -- Accelerated MRI Reconstruction with Dual-domain Generative Adversarial Network -- Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator -- Joint Multi-Anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions -- Modeling and Analysis Brain Development via Discriminative Dictionary Learning -- Deep Learning for Computed Tomography -- Virtual Thin Slice: 3D Conditional GAN-based Super-resolution for CT Slice Interval -- Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior -- Measuring CT Reconstruction Quality with Deep Convolutional Neural Networks -- Deep Learning based Metal Inpainting in the Projection Domain: Initial Results -- Deep Learning for General Image Reconstruction -- Flexible Conditional Image Generation of Missing Data with Learned Mental Maps -- Spatiotemporal PET reconstruction using ML-EM with learned diffeomorphic deformation -- Stain Style Transfer using Transitive Adversarial Networks -- Blind Deconvolution Microscopy Using Cycle Consistent CNN with Explicit PSF Layer -- Deep Learning based approach to quantification of PET tracer uptake in small tumors -- Task-GAN: Improving Generative Adversarial Network for Image Reconstruction -- Gamma Source Location Learning from Synthetic Multi-Pinhole Collimator Data -- Neural Denoising of Ultra-Low Dose Mammography -- Image Reconstruction in a Manifold of Image Patches: Application to Whole-fetus Ultrasound Imaging -- Image Super Resolution via Bilinear Pooling: Application to Confocal Endomicroscopy -- TPSDicyc: Improved Deformation Invariant Cross-domain Medical Image Synthesis -- PredictUS: A Method to Extend the Resolution-Precision Trade-off in Quantitative Ultrasound Image Reconstruction. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the Second International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. The 24 full papers presented were carefully reviewed and selected from 32 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging; deep learning for computed tomography; and deep learning for general image reconstruction. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
