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Advanced Materials for Multidisciplinary Applications / / edited by Marinda Wu, Wei Gao, Lei Li, Yingchun Lu, Jingbo Louise Liu
| Advanced Materials for Multidisciplinary Applications / / edited by Marinda Wu, Wei Gao, Lei Li, Yingchun Lu, Jingbo Louise Liu |
| Autore | Wu Marinda |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (386 pages) |
| Disciplina | 620.11 |
| Altri autori (Persone) |
GaoWei
LiLei LuYingchun LiuJingbo Louise |
| Soggetto topico |
Materials
Catalysis Force and energy Biomaterials Nanotechnology Renewable energy sources Chemistry Materials for Energy and Catalysis Renewable Energy Materials Chemistry Materials for Devices |
| ISBN | 3-031-39404-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Leadership and Resiliency in the Global Chemistry Enterprise -- Chinese American Chemical Society (CACS): Overview and 40th Anniversary -- Universal vaccine development through glycoengineering -- Advances in Lipid Nanoparticles for mRNA Delivery: from Concept to Clinical Intervention -- Biodegradable Nanoparticles for Drug Delivery -- 6-Azaindole Derivative GNF2133 as DYRK1A Inhibitor for the Treatment of Type 1 Diabetes -- Super resolution study of neurotransmission in real time with scanning electrochemical microcopy and nanoelectrodes -- Novel acrylic emulsion polymers with high bio-carbon content for personal care applications -- A Comparative Study between Gel Permeation Chromatography and Asymmetric Flow Field Flow Fractionation for Characterization of Gelatin -- Metal Derivative Reaction Engineering: A Gateway to Novel Energy Conversion Techology -- Nanohybrids: perform better and be multifunctional -- Advances in CO2 Capture and Utilization for Carbon Neutrality -- Interdisciplinary and International Cooperation to Enhance Chemical Materials for Solar Energy Conversion -- Chemical and fuel commodities through catalytic solvolysis of lignin -- New Alkali Metal Chemistry for Energy and Environments -- A Laser Photolysis Approach to Generate Hierarchically Porous MOFs -- Fabrication of Colloidal Photonic Crystal-Based Materials for Sensing and Coating Applications -- Crosslinking Chemistry for Solution Processable Multilayer Organic Light-Emitting Diodes -- Sustainability and the Chemistry Enterprise -- Design and Application of Functional Materials used in Energy Storage and Conversion -- CACS – Its Long History and Contributions to the Chemical Community. |
| Record Nr. | UNINA-9910765482503321 |
Wu Marinda
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Left Atrial and Scar Quantification and Segmentation [[electronic resource] ] : First Challenge, LAScarQS 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings / / edited by Xiahai Zhuang, Lei Li, Sihan Wang, Fuping Wu
| Left Atrial and Scar Quantification and Segmentation [[electronic resource] ] : First Challenge, LAScarQS 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings / / edited by Xiahai Zhuang, Lei Li, Sihan Wang, Fuping Wu |
| Autore | Zhuang Xiahai |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (174 pages) |
| Disciplina | 006 |
| Altri autori (Persone) |
LiLei
WangSihan WuFuping |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Image processing—Digital techniques
Computer vision Computer Imaging, Vision, Pattern Recognition and Graphics |
| ISBN | 3-031-31778-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | LASSNet: A four steps deep neural network for Left Atrial Segmentation and Scar Quantification -- Multi-Depth Boundary-Aware Left Atrial Scar Segmentation Network -- Self Pre-training with Single-scale Adapter for Left Atrial Segmentation -- UGformer for Robust Left Atrium and Scar Segmentation Across Scanners -- Automatically Segmenting the Left Atrium and Scars from LGE-MRIs Using a boundary-focused nnU-Net -- Two Stage of Histogram Matching Augmentation for Domain Generalization : Application to Left Atrial Segmentation -- Sequential Segmentation of the Left Atrium and Atrial Scars Using a Multi-scale Weight Sharing Network and Boundary-based Processing -- LA-HRNet: High-resolution network for automatic left atrial segmentation in multi-center LEG MRI -- Edge-enhanced Features Guided Joint Segmentation and Quantification of Left Atrium and Scars in LGE MRI Images -- TESSLA: Two-Stage Ensemble Scar Segmentation for the Left Atrium -- Deep U-Net architecture with curriculum learning for left atrial segmentation -- Cross-domain Segmentation of Left Atrium Based on Multi-scale Decision Level Fusion -- Using Polynomial Loss and Uncertainty Information for Robust Left Atrial and Scar Quantification and Segmentation -- Automated segmentation of the left atrium and scar using deep convolutional neural networks -- Automatic Semi-Supervised Left Atrial Segmentation using Deep-Supervision 3DResUnet with Pseudo Labeling Approach for LAScarQS 2022 Challenge. |
| Record Nr. | UNISA-996534467303316 |
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Zhuang Xiahai
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Left Atrial and Scar Quantification and Segmentation : First Challenge, LAScarQS 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings / / edited by Xiahai Zhuang, Lei Li, Sihan Wang, Fuping Wu
| Left Atrial and Scar Quantification and Segmentation : First Challenge, LAScarQS 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings / / edited by Xiahai Zhuang, Lei Li, Sihan Wang, Fuping Wu |
| Autore | Zhuang Xiahai |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (174 pages) |
| Disciplina |
006
616.120754 |
| Altri autori (Persone) |
LiLei
WangSihan WuFuping |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Image processing—Digital techniques
Computer vision Computer Imaging, Vision, Pattern Recognition and Graphics |
| ISBN | 3-031-31778-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | LASSNet: A four steps deep neural network for Left Atrial Segmentation and Scar Quantification -- Multi-Depth Boundary-Aware Left Atrial Scar Segmentation Network -- Self Pre-training with Single-scale Adapter for Left Atrial Segmentation -- UGformer for Robust Left Atrium and Scar Segmentation Across Scanners -- Automatically Segmenting the Left Atrium and Scars from LGE-MRIs Using a boundary-focused nnU-Net -- Two Stage of Histogram Matching Augmentation for Domain Generalization : Application to Left Atrial Segmentation -- Sequential Segmentation of the Left Atrium and Atrial Scars Using a Multi-scale Weight Sharing Network and Boundary-based Processing -- LA-HRNet: High-resolution network for automatic left atrial segmentation in multi-center LEG MRI -- Edge-enhanced Features Guided Joint Segmentation and Quantification of Left Atrium and Scars in LGE MRI Images -- TESSLA: Two-Stage Ensemble Scar Segmentation for the Left Atrium -- Deep U-Net architecture with curriculum learning for left atrial segmentation -- Cross-domain Segmentation of Left Atrium Based on Multi-scale Decision Level Fusion -- Using Polynomial Loss and Uncertainty Information for Robust Left Atrial and Scar Quantification and Segmentation -- Automated segmentation of the left atrium and scar using deep convolutional neural networks -- Automatic Semi-Supervised Left Atrial Segmentation using Deep-Supervision 3DResUnet with Pseudo Labeling Approach for LAScarQS 2022 Challenge. |
| Record Nr. | UNINA-9910720070703321 |
|
Zhuang Xiahai
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Myocardial pathology segmentation combining multi-sequence cardiac magnetic resonance images : first challenge, MyoPS 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings / / Xiahai Zhuang, Lei Li (edsitors)
| Myocardial pathology segmentation combining multi-sequence cardiac magnetic resonance images : first challenge, MyoPS 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings / / Xiahai Zhuang, Lei Li (edsitors) |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (VIII, 177 p. 93 illus., 79 illus. in color.) |
| Disciplina | 616.07540285 |
| Collana | Lecture notes in computer science |
| Soggetto topico | Diagnostic imaging - Data processing |
| ISBN | 3-030-65651-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Stacked BCDU-net with semantic CMR synthesis: application to Myocardial PathologySegmentation challenge -- EfficientSeg: A Simple but Efficient Solution to Myocardial Pathology Segmentation Challenge -- Two-stage Method for Segmentation of the Myocardial Scars and Edema on Multi-sequence Cardiac Magnetic Resonance -- Multi-Modality Pathology Segmentation Framework: Application to Cardiac Magnetic Resonance Images -- Myocardial Edema and Scar Segmentation using a Coarse-to-Fine Framework with Weighted Ensemble -- Exploring ensemble applications for multi-sequence myocardial pathology segmentation -- Max-Fusion U-Net for Multi-Modal Pathology Segmentation with Attention and Dynamic Resampling -- Fully automated deep learning based segmentation of normal, infarcted and edema regions from multiple cardiac MRI sequences -- CMS-UNet: Cardiac Multi-task Segmentation in MRI with a U-shaped Network -- Automatic Myocardial Scar Segmentation from Multi-Sequence Cardiac MRI using Fully Convolutional Densenet with Inception and Squeeze-Excitation Module -- Dual Attention U-net for Multi-Sequence Cardiac MR Images Segmentation -- Accurate Myocardial Pathology Segmentation with Residual U-Net -- Stacked and Parallel U-Nets with Multi-Output for Myocardial Pathology Segmentation -- Dual-path Feature Aggregation Network Combined Multi-layer Fusion for Myocardial Pathology Segmentation with Multi-sequence Cardiac MR -- Cascaded Framework with Complementary CMR Information for Myocardial Pathology Segmentation -- CMRadjustNet: Recognition and standardization of cardiac MRI orientation via multi-tasking learning and deep neural networks. |
| Record Nr. | UNINA-9910447248303321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Myocardial pathology segmentation combining multi-sequence cardiac magnetic resonance images : first challenge, MyoPS 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings / / Xiahai Zhuang, Lei Li (edsitors)
| Myocardial pathology segmentation combining multi-sequence cardiac magnetic resonance images : first challenge, MyoPS 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings / / Xiahai Zhuang, Lei Li (edsitors) |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (VIII, 177 p. 93 illus., 79 illus. in color.) |
| Disciplina | 616.07540285 |
| Collana | Lecture notes in computer science |
| Soggetto topico | Diagnostic imaging - Data processing |
| ISBN | 3-030-65651-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Stacked BCDU-net with semantic CMR synthesis: application to Myocardial PathologySegmentation challenge -- EfficientSeg: A Simple but Efficient Solution to Myocardial Pathology Segmentation Challenge -- Two-stage Method for Segmentation of the Myocardial Scars and Edema on Multi-sequence Cardiac Magnetic Resonance -- Multi-Modality Pathology Segmentation Framework: Application to Cardiac Magnetic Resonance Images -- Myocardial Edema and Scar Segmentation using a Coarse-to-Fine Framework with Weighted Ensemble -- Exploring ensemble applications for multi-sequence myocardial pathology segmentation -- Max-Fusion U-Net for Multi-Modal Pathology Segmentation with Attention and Dynamic Resampling -- Fully automated deep learning based segmentation of normal, infarcted and edema regions from multiple cardiac MRI sequences -- CMS-UNet: Cardiac Multi-task Segmentation in MRI with a U-shaped Network -- Automatic Myocardial Scar Segmentation from Multi-Sequence Cardiac MRI using Fully Convolutional Densenet with Inception and Squeeze-Excitation Module -- Dual Attention U-net for Multi-Sequence Cardiac MR Images Segmentation -- Accurate Myocardial Pathology Segmentation with Residual U-Net -- Stacked and Parallel U-Nets with Multi-Output for Myocardial Pathology Segmentation -- Dual-path Feature Aggregation Network Combined Multi-layer Fusion for Myocardial Pathology Segmentation with Multi-sequence Cardiac MR -- Cascaded Framework with Complementary CMR Information for Myocardial Pathology Segmentation -- CMRadjustNet: Recognition and standardization of cardiac MRI orientation via multi-tasking learning and deep neural networks. |
| Record Nr. | UNISA-996418294003316 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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