Multimodal Learning for Clinical Decision Support [[electronic resource] ] : 11th International Workshop, ML-CDS 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings / / edited by Tanveer Syeda-Mahmood, Xiang Li, Anant Madabhushi, Hayit Greenspan, Quanzheng Li, Richard Leahy, Bin Dong, Hongzhi Wang |
Edizione | [1st ed. 2021.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
Descrizione fisica | 1 online resource (125 pages) |
Disciplina | 616.07540285 |
Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
Soggetto topico |
Image processing - Digital techniques
Computer vision Machine learning Database management Social sciences - Data processing Computer Imaging, Vision, Pattern Recognition and Graphics Machine Learning Database Management Computer Application in Social and Behavioral Sciences |
ISBN | 3-030-89847-4 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | From Picoscale Pathology to Decascale Disease: Image Registration with a Scattering Transform and Varifolds for Manipulating Multiscale Data -- Multi-Scale Hybrid Transformer Networks: Application to Prostate Disease Classification -- Predicting Treatment Response in Prostate Cancer Patients Based on Multimodal PET/CT for Clinical Decision Support -- A Federated Multigraph Integration Approach for Connectional Brain Template Learning -- SAMA: Spatially-Aware Multimodal Network with Attention for Early Lung Cancer Diagnosis -- Fully Automatic Head and Neck Cancer Prognosis Prediction in PET/CT -- Feature Selection for Privileged Modalities in Disease Classification -- Merging and Annotating Teeth and Roots from Automated Segmentation of Multimodal Images -- Structure and Feature based Graph U-Net for Early Alzheimer's Disease Prediction -- A Method for Predicting Alzheimer's Disease based on the Fusion of Single Nucleotide Polymorphisms and Magnetic Resonance Feature Extraction. |
Record Nr. | UNISA-996464439703316 |
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. di Salerno | ||
|
Multimodal Learning for Clinical Decision Support : 11th International Workshop, ML-CDS 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings / / edited by Tanveer Syeda-Mahmood, Xiang Li, Anant Madabhushi, Hayit Greenspan, Quanzheng Li, Richard Leahy, Bin Dong, Hongzhi Wang |
Edizione | [1st ed. 2021.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
Descrizione fisica | 1 online resource (125 pages) |
Disciplina | 616.07540285 |
Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
Soggetto topico |
Image processing - Digital techniques
Computer vision Machine learning Database management Social sciences - Data processing Computer Imaging, Vision, Pattern Recognition and Graphics Machine Learning Database Management Computer Application in Social and Behavioral Sciences |
ISBN | 3-030-89847-4 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | From Picoscale Pathology to Decascale Disease: Image Registration with a Scattering Transform and Varifolds for Manipulating Multiscale Data -- Multi-Scale Hybrid Transformer Networks: Application to Prostate Disease Classification -- Predicting Treatment Response in Prostate Cancer Patients Based on Multimodal PET/CT for Clinical Decision Support -- A Federated Multigraph Integration Approach for Connectional Brain Template Learning -- SAMA: Spatially-Aware Multimodal Network with Attention for Early Lung Cancer Diagnosis -- Fully Automatic Head and Neck Cancer Prognosis Prediction in PET/CT -- Feature Selection for Privileged Modalities in Disease Classification -- Merging and Annotating Teeth and Roots from Automated Segmentation of Multimodal Images -- Structure and Feature based Graph U-Net for Early Alzheimer's Disease Prediction -- A Method for Predicting Alzheimer's Disease based on the Fusion of Single Nucleotide Polymorphisms and Magnetic Resonance Feature Extraction. |
Record Nr. | UNINA-9910506376203321 |
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|
Multiscale Multimodal Medical Imaging [[electronic resource] ] : First International Workshop, MMMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings / / edited by Quanzheng Li, Richard Leahy, Bin Dong, Xiang Li |
Edizione | [1st ed. 2020.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
Descrizione fisica | 1 online resource (x, 108 pages) : illustrations |
Disciplina | 616.0754028 |
Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
Soggetto topico |
Optical data processing
Machine learning Pattern recognition Image Processing and Computer Vision Machine Learning Pattern Recognition |
ISBN | 3-030-37969-8 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Multi-Modal Image Prediction via Spatial Hybrid U-Net -- Automatic Segmentation of Liver CT Image Based on Dense Pyramid Network -- OctopusNet: A Deep Learning Segmentation Network for Multi-modal Medical Images -- Neural Architecture Search for Optimizing Deep Belief Network Models of fMRI Data -- Feature Pyramid based Attention for Cervical Image Classification -- Single-scan Dual-tracer Separation Network Based on Pre-trained GRU -- PGU-net+: Progressive Growing of U-net+ for Automated Cervical Nuclei Segmentation -- Automated Classification of Arterioles and Venules for Retina Fundus Images using Dual Deeply-Supervised Network -- Liver Segmentation from Multimodal Images using HED-Mask R-CNN -- aEEG Signal Analysis with Ensemble Learning for Newborn Seizure Detection -- Speckle Noise Removal in Ultrasound Images Using A Deep Convolutional Neural Network and A Specially Designed Loss Function -- Automatic Sinus Surgery Skill Assessment Based on Instrument Segmentation and Tracking in Endoscopic Video -- U-Net Training with Instance-Layer Normalization. |
Record Nr. | UNISA-996418294603316 |
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. di Salerno | ||
|
Multiscale Multimodal Medical Imaging : First International Workshop, MMMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings / / edited by Quanzheng Li, Richard Leahy, Bin Dong, Xiang Li |
Edizione | [1st ed. 2020.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
Descrizione fisica | 1 online resource (x, 108 pages) : illustrations |
Disciplina |
616.0754028
616.0754 (edition:23) |
Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
Soggetto topico |
Optical data processing
Machine learning Pattern recognition Image Processing and Computer Vision Machine Learning Pattern Recognition |
ISBN | 3-030-37969-8 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Multi-Modal Image Prediction via Spatial Hybrid U-Net -- Automatic Segmentation of Liver CT Image Based on Dense Pyramid Network -- OctopusNet: A Deep Learning Segmentation Network for Multi-modal Medical Images -- Neural Architecture Search for Optimizing Deep Belief Network Models of fMRI Data -- Feature Pyramid based Attention for Cervical Image Classification -- Single-scan Dual-tracer Separation Network Based on Pre-trained GRU -- PGU-net+: Progressive Growing of U-net+ for Automated Cervical Nuclei Segmentation -- Automated Classification of Arterioles and Venules for Retina Fundus Images using Dual Deeply-Supervised Network -- Liver Segmentation from Multimodal Images using HED-Mask R-CNN -- aEEG Signal Analysis with Ensemble Learning for Newborn Seizure Detection -- Speckle Noise Removal in Ultrasound Images Using A Deep Convolutional Neural Network and A Specially Designed Loss Function -- Automatic Sinus Surgery Skill Assessment Based on Instrument Segmentation and Tracking in Endoscopic Video -- U-Net Training with Instance-Layer Normalization. |
Record Nr. | UNINA-9910366656503321 |
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|