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Machine Learning in Medical Imaging : 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings / edited by Chunfeng Lian, Xiaohuan Cao, Islem Rekik, Xuanang Xu, Pingkun Yan

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Titel Machine Learning in Medical Imaging : 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings / edited by Chunfeng Lian, Xiaohuan Cao, Islem Rekik, Xuanang Xu, Pingkun Yan
PersonLian, Chunfeng [Herausgeber/in]
Cao, Xiaohuan [Herausgeber/in]
Rekik, Islem [Herausgeber/in]
Xu, Xuanang [Herausgeber/in]
Yan, Pingkun [Herausgeber/in]
VeröffentlichungCham : Springer International Publishing, 2021.
Cham : Imprint: Springer, 2021.
Umfang / Format 1 Online-Ressource(XVIII, 704 p. 16 illus.)
Ausgabe1st ed. 2021.
SpracheEnglisch (Sprache des Textes)
LandSchweiz
ISBN9783030875893
9783030875886, 9783030875909 (Sekundärausgabe)
Nummer1772261726 (K10Plus-Nummer)
Schriftenreihe (Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 12966)
Schlagwörter Optical data processing.
Artificial intelligence.
Computers.
Pattern recognition.
Bioinformatics.
Inhalt Contrastive 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 H
Inhalt This 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.

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