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[null,null,"Titolo",null,"Machine Learning in Medical Imaging : 13th International Workshop, MLMI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings / / edited by Chunfeng Lian, Xiaohuan Cao, Islem Rekik, Xuanang Xu, Zhiming Cui"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2022"]
[null,null,"ISBN",null,"9783031210143"]
[{"$nulls":4},"303121014X"]
[null,null,"Edizione",null,"[1st ed. 2022.]"]
[null,null,"Descrizione fisica",null,"1 online resource (491 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science, , 1611-3349 ; ; 13583"]
[null,null,"Disciplina",null,"929.605"]
[{"$nulls":4},"006.37"]
[null,null,"Soggetti",null,"Computer vision"]
[{"$nulls":4},"Pattern recognition systems"]
[{"$nulls":4},"Machine learning"]
[{"$nulls":4},"Computer engineering"]
[{"$nulls":4},"Computer networks"]
[{"$nulls":4},"Social sciences - Data processing"]
[{"$nulls":4},"Bioinformatics"]
[{"$nulls":4},"Computer Vision"]
[{"$nulls":4},"Automated Pattern Recognition"]
[{"$nulls":4},"Machine Learning"]
[{"$nulls":4},"Computer Engineering and Networks"]
[{"$nulls":4},"Computer Application in Social and Behavioral Sciences"]
[{"$nulls":4},"Computational and Systems Biology"]
[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,"Function MRI Representation Learning via Self-Supervised Transformer for Automated Brain Disorder Analysis -- Predicting Age-related Macular Degeneration Progression with Longitudinal Fundus Images using Deep Learning -- Region-Guided Channel-Wise Attention Network for Accelerated MRI Reconstruction -- Student Becomes Decathlon Master in Retinal Vessel Segmentation via Dual-teacher Multi-target Domain Adaptation -- Rethinking Degradation: Radiograph Super-Resolution via AID-SRGAN -- 3D Segmentation with "]
[{"$nulls":4},"Fully Trainable Gabor Kernels and Pearson's Correlation Coefficient -- A More Design-flexible Medical Transformer for Volumetric Image Segmentation -- Dcor-VLDet: A Vertebra Landmark Detection Network for Scoliosis Assessment with Dual Coordinate System -- Plug-and-play Shape Refinement Framework for Multi-site and Lifespan Brain Skull Stripping -- A Coarse-To-Fine Network for Craniopharyngioma Segmentation -- Patch-level instance-group discrimination with pretext-invariant learning for colitis scoring -- AutoMO-Mixer: An automated multi-objective Mixer model for balanced, safe and robust prediction in medicine -- Memory transformers for full context and high-resolution 3D Medical Segmentation -- Whole Mammography Diagnosis via Multi-instance Supervised Discriminative Localization and Classification -- Cross Task Temporal Consistency for Semi Supervised Medical Image Segmentation -- U-Net vs Transformer: Is U-Net Outdated in Medical Image Registration -- UNet-eVAE: Iterative refinement using VAE embodied learning for endoscopic image segmentation -- Dynamic Linear Transformer for 3D Biomedical Image Segmentation -- Automatic Grading of Emphysema by Combining 3D Lung Tissue Appearance and Deformation Map Using a Two-stream Fully Convolutional Neural Network -- A Novel Two-Stage Multi-View Low-Rank Sparse Subspace Clustering Approach to Explore the Relationship between Brain Function and Structure -- Fast Image-Level MRI Harmonization via Spectrum Analysis -- CT2CXR: CT-based CXR Synthesis for Covid-19 Pneumonia Classification -- Harmonization of Multi-Site Cortical Data Across the Human Lifespan -- Head and neck vessel segmentation with connective topology using affinity graph -- Coarse Retinal Lesion Annotations Refinement via Prototypical Learning -- Nuclear Segmentation and Classification: On Color & Compression Generalization -- Understanding Clinical Progression of Late-Life Depression to Alzheimer’s Disease Over 5 Years with Structural MRI -- ClinicalRadioBERT: Knowledge-Infused Few Shot Learning for Clinical Notes Named Entity Recognition -- Graph Representation Neural Architecture Search for Optimal Spatial/Temporal Functional Brain Network Decomposition -- Driving Points Prediction For Abdominal Probabilistic Registration -- CircleSnake: Instance Segmentation with Circle Representation -- Vertebrae localization, segmentation and identification using a graph optimization and an anatomic consistency cycle -- Coronary Ostia Localization Using Residual U-Net with HeatmapMatching and 3D DSNT -- AMLP-Conv, a 3D Axial Long-range Interaction Multilayer Perceptron for CNNs -- Neural State-Space Modeling with Latent Causal-Effect Disentanglement -- Adaptive Unified Contrastive Learning for Imbalanced Classification -- Prediction of HPV-Associated Genetic Diversity for Squamous Cell Carcinoma of Head and Neck Cancer based on 18F-FDG PET/CT -- TransWS: Transformer-based Weakly Supervised Histology Image Segmentation -- Contextual Attention Network: Transformer Meets U-Net -- Intelligent Masking: Deep Q-Learning for Context Encoding in Medical Image Analysis -- A New Lightweight Architecture and a Class Imbalance Aware Loss Function for Multi-label Classification of Intracranial Hemorrhages -- Spherical Transformer on Cortical Surfaces -- Accurate localization of inner ear regions of interests using deep reinforcement learning -- Shifted Windows Transformers for Medical Image Quality Assessment -- Multi-scale Multi-structure Siamese Network (MMSNet) for Primary Open-angleGlaucoma Prediction -- HealNet - Self-Supervised Acute Wound Heal-Stage Classification -- Federated Tumor Segmentation with Patch-wise Deep Learning Model -- Multi-scale and Focal Region Based Deep Learning Network for Fine Brain Parcellation."]
[null,null,null,"Sommario/riassunto","This book constitutes the proceedings of the 13th International Workshop on Machine Learning in Medical Imaging, MLMI 2022, held in conjunction with MICCAI 2022, in Singapore, in September 2022. The 48 full papers presented in this volume were carefully reviewed and selected from 64 submissions. They focus on major trends and challenges in the above-mentioned area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc."]