000945168 000__ 11310cam\a2200649Mi\4500
000945168 001__ 945168
000945168 005__ 20230306152505.0
000945168 006__ m\\\\\o\\d\\\\\\\\
000945168 007__ cr\nn\nnnunnun
000945168 008__ 201001s2020\\\\sz\\\\\\o\\\\\101\0\eng\d
000945168 019__ $$a1198892781$$a1203986217$$a1224539242
000945168 020__ $$a9783030597108$$q(electronic book)
000945168 020__ $$a3030597105
000945168 020__ $$z9783030597092
000945168 035__ $$aSP(OCoLC)on1202467072
000945168 035__ $$aSP(OCoLC)1202467072$$z(OCoLC)1198892781$$z(OCoLC)1203986217$$z(OCoLC)1224539242
000945168 040__ $$aEBLCP$$beng$$erda$$epn$$cEBLCP$$dGW5XE$$dOCLCO$$dEBLCP$$dGZM$$dDCT$$dSFB$$dYDX$$dOCLCF$$dUPM
000945168 049__ $$aISEA
000945168 050_4 $$aTA1630-1650
000945168 08204 $$a006.6$$223
000945168 08204 $$a006.37$$223
000945168 1112_ $$aInternational Conference on Medical Image Computing and Computer-Assisted Intervention$$n(23rd :$$d2020 :$$cOnline)
000945168 24510 $$aMedical image computing and computer assisted intervention -- MICCAI 2020 :$$b23rd International Conference, Lima, Peru, October 4-8, 2020, Proceedings.$$nPart I /$$cAnne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz (eds.).
000945168 2463_ $$aMICCAI 2020
000945168 264_1 $$aCham :$$bSpringer,$$c2020.
000945168 300__ $$a1 online resource (XXXVII, 849 pages) :$$billustrations.
000945168 336__ $$atext$$btxt$$2rdacontent
000945168 337__ $$acomputer$$bc$$2rdamedia
000945168 338__ $$aonline resource$$bcr$$2rdacarrier
000945168 347__ $$atext file$$bPDF$$2rda
000945168 4901_ $$aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$$v12261
000945168 500__ $$aIncludes author index.
000945168 5050_ $$aMachine Learning Methodologies -- Attention, Suggestion and Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation -- Scribble2Label: Scribble-Supervised Cell Segmentation via Self-Generating Pseudo-Labels with Consistency -- Are fast labeling methods reliable? A case study of computer-aided expert annotations on microscopy slides -- Deep Reinforcement Active Learning for Medical Image Classification -- An Effective Data Refinement Approach for Upper Gastrointestinal Anatomy Recognition -- Synthetic Sample Selection via Reinforcement Learning -- Dual-level Selective Transfer Learning for Intrahepatic Cholangiocarcinoma Segmentation in Non-enhanced Abdominal CT -- BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder Architecture -- Constrain Latent Space for Schizophrenia Classification via Dual Space Mapping Net -- Have you forgotten? A method to assess ifmachine learning models have forgotten data -- Learning and Exploiting Interclass Visual Correlations for Medical Image Classification -- Feature Preserving Smoothing Provides Simple and Effective Data Augmentation for Medical Image Segmentation -- Deep kNN for Medical Image Classification -- Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration -- DECAPS: Detail-oriented Capsule Networks -- Federated Simulation for Medical Imaging -- Continual Learning of New Diseases with Dual Distillation and Ensemble Strategy -- Learning to Segment When Experts Disagree -- Deep Disentangled Hashing with Momentum Triplets for Neuroimage Search -- Learning joint shape and appearance representations with metamorphic auto-encoders -- Collaborative Learning of Cross-channel Clinical Attention for Radiotherapy-related Esophageal Fistula Prediction from CT -- Learning Bronchiole-Sensitive Airway Segmentation CNNs by Feature Recalibration and Attention Distillation -- Learning Rich Attention for Pediatric Bone Age Assessment -- Weakly Supervised Organ Localization with Attention Maps Regularized by Local Area Reconstruction -- High-order Attention Networks for Medical Image Segmentation -- NAS-SCAM: Neural Architecture Search-based Spatial and Channel Joint Attention Module for Nuclei Semantic Segmentation and Classification -- Scientific Discovery by Generating Counterfactuals using Image Translation -- Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction -- Encoding Visual Attributes in Capsules for Explainable Medical Diagnoses -- Interpretability-guided Content-based Medical Image Retrieval -- Domain aware medical image classifier interpretation by counterfactual impact analysis -- Towards Emergent Language Symbolic Semantic Segmentation and Model Interpretability -- Meta Corrupted Pixels Mining for Medical Image Segmentation -- UXNet: Searching Multi-level Feature Aggregation for 3D Medical Image Segmentation -- Difficulty-aware Meta-learning for Rare Disease Diagnosis -- Few Is Enough: Task-Augmented Active Meta-Learning for Brain Cell Classification -- Automatic Data Augmentation for 3D Medical Image Segmentation -- MS-NAS: Multi-Scale Neural Architecture Search for Medical Image Segmentation -- Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs By Comparing Image Representations -- Dual-task Self-supervision for Cross-Modality Domain Adaptation -- Dual-Teacher: Integrating Intra-domain and Inter-domain Teachers for Annotation-efficient Cardiac Segmentation -- Test-time Unsupervised Domain Adaptation -- Self domain adapted network -- Entropy Guided Unsupervised Domain Adaptation for Cross-Center Hip Cartilage Segmentation from MRI -- User-Guided Domain Adaptation for Rapid Annotation from User Interactions: A Study on Pathological Liver Segmentation -- SALAD: Self-Supervised Aggregation Learning for Anomaly Detection on X-Rays -- Scribble-based Domain Adaptation via Deep Co-Segmentation -- Source-Relaxed Domain Adaptation for Image Segmentation -- Region-of-interest guided Supervoxel Inpainting for Self-supervision -- Harnessing Uncertainty in Domain Adaptation for MRI Prostate Lesion Segmentation -- Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation -- DMNet: Difference Minimization Network for Semi-supervised Segmentation in Medical Images -- Double-uncertainty Weighted Method for Semi-supervised Learning -- Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images -- Local and Global Structure-aware Entropy Regularized Mean Teacher Model for 3D Left Atrium segmentation -- Improving dense pixelwise prediction of epithelial density using unsupervised data augmentation for consistency regularization -- Knowledge-guided Pretext Learning for Utero-placental Interface Detection -- Self-supervised Depth Estimation to Regularise Semantic Segmentation in Knee Arthroscopy -- Semi-supervised Medical Image Classification with Global Latent Mixing -- Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-Supervised Medical Image Segmentation -- Semi-Supervised Classification of Diagnostic Radiographs with NoTeacher: A Teacher that is not Mean -- Predicting Potential Propensity of Adolescents to Drugs via New Semi-Supervised Deep Ordinal Regression Model -- Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning and Dual-UNet -- Domain Adaptive Relational Reasoning for 3D Multi-Organ Segmentation -- Realistic Adversarial Data Augmentation for MR Image Segmentation -- Learning to Segment Anatomical Structures Accurately from One Exemplar -- Uncertainty estimates as data selection criteria to boost omni-supervised learning -- Extreme Consistency: Overcoming Annotation Scarcity and Domain Shifts -- Spatio-temporal Consistency and Negative LabelTransfer for 3D freehand US Segmentation -- Characterizing Label Errors: Confident Learning for Noisy-labeled Image Segmentation -- Leveraging Undiagnosed Data for Glaucoma Classification with Teacher-Student Learning -- Difficulty-aware Glaucoma Classification with Multi-Rater Consensus Modeling -- Intra-operative Forecasting of Growth Modulation Spine Surgery Outcomes with Spatio-Temporal Dynamic Networks -- Self-supervision on Unlabelled OR Data for Multi-person 2D/3D Human Pose Estimation -- Knowledge distillation from multi-modal to mono-modal segmentation networks -- Heterogeneity Measurement of Cardiac Tissues Leveraging Uncertainty Information from Image Segmentation -- Efficient Shapley Explanation For Features Importance Estimation Under Uncertainty -- Cartilage Segmentation in High-Resolution 3D Micro-CT Images via Uncertainty-Guided Self-Training with Very Sparse Annotation -- Probabilistic 3D surface reconstruction from sparse MRI information -- Can you trust predictive uncertainty under real dataset shifts in digital pathology? -- Deep Generative Model for Synthetic-CT Generation with Uncertainty Predictions.
000945168 506__ $$aAccess limited to authorized users.
000945168 520__ $$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 VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography.
000945168 5880_ $$aOnline resource; title from PDF title page (SpringerLink, viewed December 2, 2020).
000945168 650_0 $$aDiagnostic imaging$$xData processing$$vCongresses.
000945168 650_0 $$aOptical data processing.
000945168 650_0 $$aArtificial intelligence.
000945168 650_0 $$aApplication software.
000945168 650_0 $$aEducation$$xData processing.
000945168 650_0 $$aBioinformatics.
000945168 650_0 $$aPattern perception.
000945168 7001_ $$aMartel, Anne L.,$$eeditor.
000945168 7001_ $$aAbolmaesumi, Purang,$$eeditor.
000945168 7001_ $$aStoyanov, Danail,$$eeditor.
000945168 7001_ $$aMateus, Diana.,$$eeditor.
000945168 7001_ $$aZuluaga, Maria A.,$$eeditor.
000945168 7001_ $$aZhou, S. Kevin,$$eeditor.
000945168 7001_ $$aRacoceanu, Daniel,$$eeditor.
000945168 7001_ $$aJoskowicz, Leo.,$$eeditor.
000945168 77608 $$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 I$$dCham : Springer International Publishing AG,c2020$$z9783030597092
000945168 830_0 $$aLNCS sublibrary.$$nSL 6,$$pImage processing, computer vision, pattern recognition, and graphics ;$$v12261.
000945168 852__ $$bebk
000945168 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-030-59710-8$$zOnline Access$$91397441.1
000945168 909CO $$ooai:library.usi.edu:945168$$pGLOBAL_SET
000945168 980__ $$aEBOOK
000945168 980__ $$aBIB
000945168 982__ $$aEbook
000945168 983__ $$aOnline
000945168 994__ $$a92$$bISE