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[null,"1.","Record Nr.",null,"UNIORUON00195040"]
[null,null,"Autore",null,"MURATOV, Pavel Pavlovič"]
[null,null,"Titolo",null,"Obrazy Italii / P. P. Muratov ; red., komm. i posleslovie V. N. Graščenkova"]
[null,null,"Pubbl/distr/stampa",null,"Moskva, : Galart, 1993-1994"]
[null,null,"Descrizione fisica",null,"2 v. : tav. ; 24 cm."]
[null,null,"Disciplina",null,"759.7"]
[null,null,"Soggetti",null,"Italia nella pittura russa"]
[null,null,"Lingua di pubblicazione",null,"Russo"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"2.","Record Nr.",null,"UNINA9910595048303321"]
[null,null,"Titolo",null,"Simplifying Medical Ultrasound : Third International Workshop, ASMUS 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings / / edited by Stephen Aylward, J. Alison Noble, Yipeng Hu, Su-Lin Lee, Zachary Baum, Zhe Min"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022"]
[null,null,"ISBN",null,"9783031169021"]
[{"$nulls":4},"3031169026"]
[null,null,"Edizione",null,"[1st ed. 2022.]"]
[null,null,"Descrizione fisica",null,"1 online resource (202 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science, , 1611-3349 ; ; 13565"]
[null,null,"Disciplina",null,"616.07543"]
[null,null,"Soggetti",null,"Image processing - Digital techniques"]
[{"$nulls":4},"Computer vision"]
[{"$nulls":4},"Computer engineering"]
[{"$nulls":4},"Computer networks"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Application software"]
[{"$nulls":4},"Computer Imaging, Vision, Pattern Recognition and Graphics"]
[{"$nulls":4},"Computer Engineering and Networks"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Computer and Information Systems Applications"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Classification and Detection -- Rapid Lung Ultrasound COVID-19 Severity Scoring with Resource-Efficient Deep Feature Extraction -- Spatio-temporal model for EUS video detection of Pancreatic Anatomy Structures -- RL based Unsupervised Video Summarization framework for Ultrasound Imaging -- Prediction of Kidney Transplant Function with Machine Learning from Computational Ultrasound Features -- Differential Learning from Sparse and Noisy Labels for Robust Detection of Clinical Landmarks in Echo Cine Series -- End-to-End Myocardial Infarction Classification from Echocardiographic Scans -- View Classification of Color Doppler Echocardiography via Automatic Alignment between Doppler and B-mode Imaging -- Segmentation and Reconstruction -- AI-enabled Assessment of Cardiac Systolic and "]
[{"$nulls":4},"Diastolic Function from Echocardiography -- 3D Cardiac Anatomy Reconstruction from 2D Segmentations: a Study using Synthetic Data -- Left Ventricle Contouring of Apical Three-Chamber Views on 2D Echocardiography -- Adnexal Mass Segmentation with Ultrasound Data Synthesis -- Self-Knowledge Distillation for First Trimester Ultrasound Saliency Prediction -- A Universal End-to-End Universal Description of Pulse-Echo Ultrasound Image Reconstruction -- Assessment, Guidance and Robotics -- Learning Generalized Non-Rigid Multimodal Biomedical Image Registration from Generic Point Set Data -- Contact force Prediction for a Robotic Transesophageal Ultrasound Probe via Operating Torque Sensing -- Meta-Registration: Learning Test-Time Optimization for Single-Pair Image Registration -- Automatic Quality Assessment of First Trimester Crown-Rump-Length Ultrasound Images -- Towards Multi-Modal Self-Supervised Video and Ultrasound Pose Estimation for Laparoscopic Liver Surgery."]
[null,null,null,"Sommario/riassunto","Chapters \"Left Ventricle Contouring of Apical Three-Chamber Views on 2D Echocardiography\" and \"3D Cardiac Anatomy Reconstruction from 2D Segmentations: a Study using Synthetic Data\" are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com."]