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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 /
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...
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| Other Authors: | , , , , |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Cham :
Springer International Publishing : Imprint: Springer,
2021.
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| Edition: | 1st ed. 2021. |
| Series: | LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ;
12966. |
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MARC
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| 245 | 0 | 0 | |a Machine Learning in Medical Imaging |h [electronic resource] : |b 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings / |c edited by Chunfeng Lian, Xiaohuan Cao, Islem Rekik, Xuanang Xu, Pingkun Yan. |
| 250 | |a 1st ed. 2021. | ||
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| 490 | 1 | |a Image Processing, Computer Vision, Pattern Recognition, and Graphics ; |v 12966 | |
| 505 | 0 | |a 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 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. | |
| 520 | |a 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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| 650 | 0 | |a Artificial intelligence. |0 sh 85008180 | |
| 650 | 0 | |a Computers. |0 sh 85029552 | |
| 650 | 0 | |a Pattern perception. |0 sh 85098789 | |
| 650 | 0 | |a Bioinformatics. |0 sh 00003585 | |
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| 700 | 1 | |a Xu, Xuanang, |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
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