000946557 000__ 11480cam\a2200637Ia\4500
000946557 001__ 946557
000946557 005__ 20230306152451.0
000946557 006__ m\\\\\o\\d\\\\\\\\
000946557 007__ cr\un\nnnunnun
000946557 008__ 201031s2020\\\\sz\\\\\\o\\\\\101\0\eng\d
000946557 019__ $$a1203997771$$a1224538024
000946557 020__ $$a9783030597252$$q(electronic book)
000946557 020__ $$a3030597253$$q(electronic book)
000946557 020__ $$z9783030597245
000946557 0247_ $$a10.1007/978-3-030-59725-2$$2doi
000946557 035__ $$aSP(OCoLC)on1202462059
000946557 035__ $$aSP(OCoLC)1202462059$$z(OCoLC)1203997771$$z(OCoLC)1224538024
000946557 040__ $$aEBLCP$$beng$$cEBLCP$$dGW5XE$$dOCLCO$$dEBLCP$$dDCT$$dSFB$$dGZM$$dOCLCF$$dUPM
000946557 049__ $$aISEA
000946557 050_4 $$aRC78.7.D53$$bB87 2020eb
000946557 08204 $$a616.07/54$$223
000946557 1112_ $$aInternational Conference on Medical Image Computing and Computer-Assisted Intervention$$n(23rd :$$d2020 :$$cOnline)
000946557 24510 $$aMedical image computing and computer assisted intervention -- MICCAI 2020 :$$b23rd International Conference, Lima, Peru, October 4-8, 2020, Proceedings.$$nPart VI /$$cAnne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz (eds.).
000946557 2463_ $$aMICCAI 2020
000946557 260__ $$aCham :$$bSpringer,$$c2020.
000946557 300__ $$a1 online resource (847 p.).
000946557 336__ $$atext$$btxt$$2rdacontent
000946557 337__ $$acomputer$$bc$$2rdamedia
000946557 338__ $$aonline resource$$bcr$$2rdacarrier
000946557 347__ $$bPDF$$2rda
000946557 347__ $$atext file$$2rdaft$$0http://rdaregistry.info/termList/fileType/1002
000946557 4901_ $$aLecture notes in computer science ;$$v12266
000946557 4901_ $$aLNCS sublibrary, SL 6, Image processing, computer vision, pattern recognition, and graphics
000946557 500__ $$aInternational conference proceedings.
000946557 500__ $$a"The conference was held through a virtual conference management platform, consisting of the main scientific program in addition to featuring 25 work-shops, 8 tutorials, and 24 challenges during October 4-8, 2020."-- Preface.
000946557 500__ $$aIncludes author index.
000946557 5050_ $$aAngiography and Vessel Analysis -- Lightweight Double Attention-fused Networks for Intraoperative Stent Segmentation -- TopNet: Topology Preserving Metric Learning for Vessel Tree Reconstruction and Labelling -- Learning Hybrid Representations for Automatic 3D Vessel Centerline Extraction -- Branch-aware Double DQN for Centerline Extraction in Coronary CT Angiography -- Automatic CAD-RADS Scoring from CCTA Scans using Deep Learning -- Higher-Order Flux with Spherical Harmonics Transform for Vascular Analysis -- Cerebrovascular Segmentation in MRA via Reverse Edge Attention Network -- Automated Intracranial Artery Labeling using a Graph Neural Network and Hierarchical Refinement -- Time matters: Handling spatio-temporal perfusion information for automated TICI scoring -- ID-Fit: Intra-saccular Device adjustment for personalized cerebral aneurysm treatment -- JointVesselNet: Joint Volume-Projection Convolutional Embedding Networks for 3D Cerebrovascular Segmentation -- Classification of Retinal Vessels into Artery-Vein in OCT Angiography Guided by Fundus Images -- Vascular surface segmentation for intracranial aneurysm isolation and quantification -- Breast Imaging -- Deep Doubly Supervised Transfer Network for Diagnosis of Breast Cancer with Imbalanced Ultrasound Imaging Modalities -- 2D X-ray mammography and 3D breast MRI registration -- A Second-order Subregion Pooling Network for Breast Ultrasound Lesion Segmentation -- Multi-Scale Gradational-Order Fusion Framework for Breast lesions Classification Using Ultrasound images -- Computer-aided Tumor Diagnosis in Automated Breast Ultrasound using 3D Detection Network -- Auto-weighting for Breast Cancer Classification in Multimodal Ultrasound -- MommiNet: Mammographic Multi-View Mass Identification Networks -- Multi-Site Evaluation of a Study-Level Classifier for Mammography using Deep Learning -- The case of missed cancers: Applying AI as a radiologist's safety net -- Decoupling Inherent Risk and Early Cancer Signs in Image-based Breast Cancer Risk Models -- Multi-task learning for detection and classification of cancer in screening mammography -- Colonoscopy -- Adaptive Context Selection for Polyp Segmentation -- PraNet: Parallel Reverse Attention Network for Polyp Segmentation -- Few-Shot Anomaly Detection for Polyp Frames from Colonoscopy -- PolypSeg: an Efficient Context-aware Network for Polyp Segmentation from Colonoscopy Videos -- Endoscopic polyp segmentation using a hybrid 2D/3D CNN -- Dermatology -- A distance-based loss for smooth and continuous skin layer segmentation in optoacoustic images -- Fairness of Classifiers Across Skin Tones in Dermatology -- Alleviating the Incompatibility between Cross Entropy Loss and Episode Training for Few-shot Skin Disease Classification -- Clinical-Inspired Network for Skin Lesion Recognition -- Multi-class Skin Lesion Segmentation for Cutaneous T-cell Lymphomas on High-Resolution Clinical Images -- Fetal Imaging -- Deep learning automatic fetal structures segmentation in MRI scans with few annotated datasets -- Data-Driven Multi-Contrast Spectral Microstructure Imaging with InSpect -- Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI consistency -- Enhanced detection of fetal pose in 3D MRI by Deep Reinforcement Learning with physical structure priors on anatomy -- Automatic angle of progress measurement of intrapartum transperineal ultrasound image with deep learning -- Joint Image Quality Assessment and Brain Extraction of Fetal MRI using Deep Learning -- Heart and Lung Imaging -- Accelerated 4D Respiratory Motion-resolved Cardiac MRI with a Model-based Variational Network -- Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation -- ICA-UNet: ICA Inspired Statistical UNet for Real-time 3D Cardiac Cine MRI Segmentation -- A Bottom-up Approach for Real-time Mitral Valve Annulus Modeling on 3D Echo Images -- A Semi-supervised Joint Network for Simultaneous Left Ventricular Motion Tracking and Segmentation in 4D Echocardiography -- Joint data imputation and mechanistic modelling for simulating heart-brain interactions in incomplete datasets -- Learning Geometry-Dependent and Physics-Based Inverse Image Reconstruction -- Hierarchical Classification of Pulmonary Lesions: A Large-Scale Radio-Pathomics Study -- Learning Tumor Growth via Follow-Up Volume Prediction for Lung Nodules -- Multi-stream Progressive Up-sampling Network for Dense CT Image Reconstruction -- Abnormality Detection in Chest X-ray Images Using Uncertainty Prediction Autoencoders -- Region Proposals for Saliency Map Refinement for Weakly-supervised Disease Localisation and Classification -- CPM-Net: A 3D Center-Points Matching Network for Pulmonary Nodule Detection in CT Scans -- Interpretable Identification of Interstitial Lung Diseases (ILD) Associated Findings from CT -- Learning with Sure Data for Nodule-Level Lung Cancer Prediction -- Cascaded Robust Learning at Imperfect Labels for Chest X-ray Segmentation -- Class-Aware Multi-Window Adversarial Lung Nodule Synthesis Conditioned on Semantic Features -- Nodule2vec: a 3D Deep Learning System for Pulmonary Nodule Retrieval Using Semantic Representation -- Deep Active Learning for Effective Pulmonary Nodule Detection -- Musculoskeletal Imaging -- Towards Robust Bone Age Assessment: Rethinking Label Noise and Ambiguity -- Improve bone age assessment by learning from anatomical local regions -- An Analysis by Synthesis Method that Allows Accurate Spatial Modeling of Thickness of Cortical Bone from Clinical QCT -- Segmentation of Paraspinal Muscles at Varied Lumbar Spinal Levels by Explicit Saliency-Aware Learning -- Manifold Ordinal-Mixup for Ordered Classes inTW3-based Bone Age Assessment -- Contour-based Bone Axis Detection for X-Ray Guided Surgery on the Knee -- Automatic Segmentation, Localization, and Identification of Vertebrae in 3D CT Images Using Cascaded Convolutional Neural Networks -- Discriminative dictionary-embedded network for comprehensive vertebrae tumor diagnosis -- Multi-vertebrae segmentation from arbitrary spine MR images under global view -- A Convolutional Approach to Vertebrae Identification in Whole Spine MRI -- Keypoints Localization for Joint Vertebra Detection and Fracture Severity Quantification -- Grading Loss: A Fracture Grade-based Metric Loss for Vertebral Fracture Detection -- 3D Convolutional Sequence to Sequence Model for Vertebral Compression Fractures Identification in CT -- SIMBA: Specific Identity Markers for Bone Age Assessment -- Doctor Imitator: A Graph-based Bone Age Assessment Framework Using Hand Radiographs -- Inferring the 3D Standing Spine Posture from 2D Radiographs -- Generative Modelling of 3D in-silico Spongiosa with Controllable Micro-Structural Parameters -- GAN-based Realistic Bone Ultrasound Image and Label Synthesis for Improved Segmentation -- Robust Bone Shadow Segmentation from 2D Ultrasound Through Task Decomposition.
000946557 506__ $$aAccess limited to authorized users.
000946557 520__ $$aThe seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic. The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: Machine learning methodologies Part II: Image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis Part IV: Segmentation; shape models and landmark detection Part V: Biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology Part VI: Angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging Part VII: Brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography.
000946557 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed December 2, 2020).
000946557 650_0 $$aDiagnostic imaging$$xData processing$$vCongresses.
000946557 7001_ $$aMartel, Anne.
000946557 7001_ $$aAbolmaesumi, Purang.
000946557 7001_ $$aStoyanov, Danail.
000946557 7001_ $$aMateus, Diana.
000946557 7001_ $$aZuluaga, Maria A.
000946557 7001_ $$aZhou, S. Kevin.
000946557 7001_ $$aRacoceanu, Daniel.
000946557 7001_ $$aJoskowicz, Leo.
000946557 77608 $$iPrint version:$$aMartel, Anne L.$$tMedical Image Computing and Computer Assisted Intervention - MICCAI 2020 : 23rd International Conference, Lima, Peru, October 4-8, 2020, Proceedings, Part VI$$dCham : Springer International Publishing AG,c2020$$z9783030597245
000946557 830_0 $$aLecture notes in computer science ;$$v12266.
000946557 830_0 $$aLNCS sublibrary.$$nSL 6,$$pImage processing, computer vision, pattern recognition, and graphics.
000946557 852__ $$bebk
000946557 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-030-59725-2$$zOnline Access$$91397441.1
000946557 909CO $$ooai:library.usi.edu:946557$$pGLOBAL_SET
000946557 980__ $$aEBOOK
000946557 980__ $$aBIB
000946557 982__ $$aEbook
000946557 983__ $$aOnline
000946557 994__ $$a92$$bISE