| 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 |
|---|
| Person | Lian, Chunfeng [Herausgeber/in]
Cao, Xiaohuan [Herausgeber/in]
Rekik, Islem [Herausgeber/in]
Xu, Xuanang [Herausgeber/in]
Yan, Pingkun [Herausgeber/in] |
|---|
| Veröffentlichung | Cham : Springer International Publishing, 2021.
Cham : Imprint: Springer, 2021. |
|---|
| Umfang / Format | 1 Online-Ressource(XVIII, 704 p. 16 illus.) |
|---|
| Ausgabe | 1st ed. 2021. |
|---|
| Sprache | Englisch (Sprache des Textes) |
|---|
| Land | Schweiz |
|---|
| ISBN | 9783030875893
9783030875886, 9783030875909 (Sekundärausgabe) |
|---|
| Nummer | 1772261726 (K10Plus-Nummer) |
|---|
| Schriftenreihe | (Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 12966) |
|---|
| Schlagwörter | Optical data processing.
Computers.
Pattern recognition.
Artificial intelligence.
Bioinformatics.
Computer engineering.
Computer networks .
Pattern recognition systems.
Computer vision. |
|---|
| 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. |
|---|