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001439987 001__ 1439987
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001439987 019__ $$a1269615576
001439987 020__ $$a9783030875893$$q(electronic bk.)
001439987 020__ $$a303087589X$$q(electronic bk.)
001439987 020__ $$z9783030875886
001439987 020__ $$z3030875881
001439987 0247_ $$a10.1007/978-3-030-87589-3$$2doi
001439987 035__ $$aSP(OCoLC)1269482863
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001439987 049__ $$aISEA
001439987 050_4 $$aRC78.7.D53$$bM56 2021
001439987 08204 $$a006.6$$223
001439987 1112_ $$aMLMI (Workshop)$$n(12th :$$d2021 :$$cOnline)
001439987 24510 $$aMachine learning in medical imaging :$$b12th International Workshop, MLMI 2021 : held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021 : proceedings /$$cChunfeng Lian, Xiaohuan Cao, Islem Rekik, Xuanang Xu, Pingkun Tan (eds.).
001439987 24630 $$aMLMI 2021
001439987 264_1 $$aCham :$$bSpringer,$$c[2021]
001439987 264_4 $$c©2021
001439987 300__ $$a1 online resource :$$billustrations
001439987 336__ $$atext$$btxt$$2rdacontent
001439987 337__ $$acomputer$$bc$$2rdamedia
001439987 338__ $$aonline resource$$bcr$$2rdacarrier
001439987 4901_ $$aLecture notes in computer science ;$$v12966
001439987 4901_ $$aLNCS sublibrary: SL6 - Image processing, computer vision, pattern recognition, and graphics
001439987 500__ $$aInternational conference proceedings.
001439987 500__ $$aIncludes author index.
001439987 5050_ $$aContrastive Representations for Continual Learning of Fine-grained Histology Images -- Learning Transferable 3D-CNN for MRI-based Brain Disorder Classification from Scratch: An Empirical Study -- Knee Cartilages Segmentation Based on Multi-scale Cascaded Neural Networks -- Deep PET/CT fusion with Dempster-Shafer theory for lymphoma segmentation -- Interpretable Histopathology Image Diagnosis via Whole Tissue Slide Level Supervision -- Variational Encoding and Decoding for Hybrid Supervision of Registration Network -- Multiresolution Registration Network (MRN) Hierarchy with Prior Knowledge Learning -- Learning to Synthesize 7T MRI from 3T MRI with Few Data by Deformable Augmentation -- Rethinking Pulmonary Nodule Detection in Multi-view 3D CT Point Cloud Representation -- End-to-end lung nodule detection framework with model-based feature projection block -- Learning Structure from Visual SemanticFeatures and Radiology Ontology for LymphNode Classification on MRI -- Improving Joint Learning of Chest X-Ray and Radiology Report by Word Region Alignment -- Cell Counting by a Location-Aware Network -- Exploring Gyro-Sulcal Functional Connectivity Differences across Task Domains via Anatomy-Guided Spatio-Temporal Graph Convolutional Networks -- StairwayGraphNet for Inter- and Intra-modality Multi-resolution Brain Graph Alignment and Synthesis -- Multi-Feature Semi-Supervised Learning for COVID-19 Diagnosis from Chest X-ray Images -- Transfer learning with a layer dependent regularization for medical image segmentation -- Multi-Scale Self-Supervised Learning for Multi-Site Pediatric Brain MR Image Segmentation with Motion/Gibbs Artifacts -- Deep active learning for dual-view mammogram analysis -- Statistical Dependency Guided Contrastive Learning for Multiple Labeling in Prenatal Ultrasound -- Semi-supervised Learning Regularized by Adversarial Perturbation and Diversity Maximization -- TransforMesh: A Transformer Network for Longitudinal Modeling of Anatomical Meshes -- A Recurrent Two-stage Anatomy-guided Network for Registration of Liver DCE-MRI -- Learning Infancy Brain Developmental Connectivity for the Cognitive Score Prediction -- Hierarchical 3D Feature Learning for Pancreas Segmentation -- Voxel-wise Cross-Volume Representation Learning for 3D Neuron Reconstruction -- Diagnosis of Hippocampal Sclerosis from Clinical Routine Head MR Images using Structure-Constrained Super-Resolution Network -- U-Net Transformer: Self and Cross Attention for Medical Image Segmentation -- Pre-biopsy multi-class classification of breast lesion pathology in mammograms -- Co-Segmentation of Multi-Modality Spinal Images Using Channel and Spatial Attention -- Hetero-Modal Learning and Expansive Consistency Constraints for Semi-Supervised Detection from Multi-Sequence Data -- STRUDEL: Self-Training with Uncertainty Dependent Label Refinement across Domains -- Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia Assessment -- MIST GAN: Modality Imputation using Style Transfer for MRI -- Biased Extrapolation in Latent Space for Imbalanced Deep Learning -- 3DMeT: 3D Medical Image Transformer for Knee Cartilage Defect Assessment -- A Gaussian Process Model for Unsupervised Analysis of High Dimensional Shape Data -- Standardized Analysis of Kidney Ultrasound Images for the Prediction of Pediatric Hydronephrosis Severity -- Automated deep learning-based detection of osteoporotic fractures in CT images -- GT U-Net: A U-Net Like Group Transformer Network for Tooth Root Segmentation -- Information Bottleneck Attribution for Visual Explanations of Diagnosis and Prognosis -- Stacked Hourglass Network with a Multi-level Attention Mechanism: Where to Look for Intervertebral Disc Labeling -- TED-net: Convolution-free T2T Vision Transformer-based Encoder-decoder Dilation network for Low-dose CT Denoising -- Self-supervised Mean Teacher for Semi-supervisedChest X-ray Classification -- VoxelEmbed: 3D Instance Segmentation and Tracking with Voxel Embedding based Deep Learning -- Using Spatio-Temporal Correlation based Hybrid Plug-and-Play Priors (SEABUS) for Accelerated Dynamic Cardiac Cine MRI -- Window-Level is a Strong Denoising Surrogate -- Cardiovascular disease risk improves COVID-19 patient outcome prediction -- Self-Supervision Based Dual-Transformation Learning for Stain Normalization, Classification and Segmentation -- Deep Representation Learning for Image-Based Cell Profiling -- Detecting Extremely Small Lesions with Point Annotations via Multi-task Learning -- Morphology-guided Prostate MRI Segmentation with Multi-slice Association -- Unsupervised Cross-modality Cardiac Image Segmentation via Disentangled Representation Learning and Consistency Regularization -- Landmark-Guided Rigid Registration for Temporomandibular Joint MRI-CBCT Images with Large Field-of-View Difference -- Spine-rib Segmentation and Labeling via Hierarchical Matching and Rib-guided Registration -- Multi-scale Segmentation Network for Rib Fracture Classification from CT Images -- Knowledge-guided Multiview Deep Curriculum Learning for Elbow Fracture Classification -- Contrastive Learning of Single-Cell Phenotypic Representations for Treatment Classification -- CorLab-Net: Anatomical Dependency-Aware Point-Cloud Learning for Automatic Labeling of Coronary Arteries -- A Hybrid Deep Registration of MR Scans to Interventional Ultrasound for Neurosurgical Guidance -- Segmentation of Peripancreatic Arteries in Multispectral Computed Tomography Imaging -- SkullEngine: A Multi-Stage CNN Framework for Collaborative CBCT Image Segmentation and Landmark Detection -- Skull Segmentation from CBCT Images via Voxel-based Rendering -- Alzheimer's Disease Diagnosis via Deep Factorization Machine Models -- 3D Temporomandibular Joint CBCT Image Segmentation via Multi-directional Resampling Ensemble Learning Network -- Vox2Surf: Implicit Surface Reconstruction from Volumetric Data -- Clinically Correct Report Generation from Chest X-rays using Templates -- Extracting Sequential Features from Dynamic Connectivity Network with rs-fMRI Data for AD Classification -- Integration of Handcrafted and Embedded Features from Functional Connectivity Network with rs-fMRI for Brain Disease Classification -- Detection of Lymph Nodes in T2 MRI using Neural Network Ensembles -- Seeking an Optimal Approach for Computer-Aided Pulmonary Embolism Detection.
001439987 506__ $$aAccess limited to authorized users.
001439987 520__ $$aThis book constitutes the proceedings of the 12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held in conjunction with MICCAI 2021, in Strasbourg, France, in September 2021.* The 71 papers presented in this volume were carefully reviewed and selected from 92 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. *The workshop was held virtually.
001439987 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 1, 2021).
001439987 650_0 $$aMachine learning$$vCongresses.
001439987 650_0 $$aDiagnostic imaging$$xData processing$$vCongresses.
001439987 650_0 $$aArtificial intelligence$$xMedical applications$$vCongresses.
001439987 650_6 $$aApprentissage automatique$$vCongrès.
001439987 650_6 $$aImagerie pour le diagnostic$$xInformatique$$vCongrès.
001439987 650_6 $$aIntelligence artificielle en médecine$$vCongrès.
001439987 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001439987 655_7 $$aConference papers and proceedings.$$2lcgft
001439987 655_7 $$aActes de congrès.$$2rvmgf
001439987 655_0 $$aElectronic books.
001439987 7001_ $$aLian, Chunfeng,$$eeditor.
001439987 7001_ $$aCao, Xiaohuan,$$eeditor.
001439987 7001_ $$aRekik, Islem,$$eeditor.
001439987 7001_ $$aXu, Xuanang,$$eeditor.
001439987 7001_ $$aYan, Pingkun,$$eeditor.
001439987 7112_ $$aInternational Conference on Medical Image Computing and Computer-Assisted Intervention$$n(24th :$$d2021 :$$cOnline)
001439987 830_0 $$aLecture notes in computer science ;$$v12966.
001439987 830_0 $$aLNCS sublibrary.$$nSL 6,$$pImage processing, computer vision, pattern recognition, and graphics.
001439987 852__ $$bebk
001439987 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-87589-3$$zOnline Access$$91397441.1
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001439987 980__ $$aBIB
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