|
|
|
|
| LEADER |
00000cam a2200000 i 4500 |
| 001 |
on1269482863 |
| 003 |
OCoLC |
| 005 |
20240623213015.0 |
| 006 |
m o d |
| 007 |
cr |n||||||||| |
| 008 |
210929s2021 sz a o 101 0 eng d |
| 040 |
|
|
|a YDX
|b eng
|e rda
|e pn
|c YDX
|d GW5XE
|d EBLCP
|d OCLCO
|d OCLCF
|d OCLCO
|d OCLCQ
|d COM
|d OCLCO
|d OCLCQ
|d OCLCO
|d OCLCL
|
| 019 |
|
|
|a 1269615576
|
| 020 |
|
|
|a 9783030875893
|q (electronic bk.)
|
| 020 |
|
|
|a 303087589X
|q (electronic bk.)
|
| 020 |
|
|
|z 9783030875886
|
| 020 |
|
|
|z 3030875881
|
| 024 |
7 |
|
|a 10.1007/978-3-030-87589-3
|2 doi
|
| 035 |
|
|
|a (OCoLC)1269482863
|z (OCoLC)1269615576
|
| 050 |
|
4 |
|a RC78.7.D53
|b M56 2021
|
| 072 |
|
7 |
|a UYT
|2 bicssc
|
| 072 |
|
7 |
|a COM012000
|2 bisacsh
|
| 072 |
|
7 |
|a UYT
|2 thema
|
| 072 |
|
7 |
|a UYQV
|2 thema
|
| 049 |
|
|
|a HCDD
|
| 111 |
2 |
|
|a MLMI (Workshop)
|n (12th :
|d 2021 :
|c Online)
|
| 245 |
1 |
0 |
|a Machine learning in medical imaging :
|b 12th International Workshop, MLMI 2021 : held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021 : proceedings /
|c Chunfeng Lian, Xiaohuan Cao, Islem Rekik, Xuanang Xu, Pingkun Tan (eds.).
|
| 246 |
3 |
0 |
|a MLMI 2021
|
| 264 |
|
1 |
|a Cham :
|b Springer,
|c [2021]
|
| 264 |
|
4 |
|c ©2021
|
| 300 |
|
|
|a 1 online resource :
|b illustrations
|
| 336 |
|
|
|a text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a computer
|b c
|2 rdamedia
|
| 338 |
|
|
|a online resource
|b cr
|2 rdacarrier
|
| 490 |
1 |
|
|a Lecture notes in computer science ;
|v 12966
|
| 490 |
1 |
|
|a LNCS sublibrary: SL6 - Image processing, computer vision, pattern recognition, and graphics
|
| 500 |
|
|
|a International conference proceedings.
|
| 500 |
|
|
|a Includes author index.
|
| 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.
|
| 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.
|
| 588 |
0 |
|
|a Online resource; title from PDF title page (SpringerLink, viewed October 1, 2021).
|
| 650 |
|
0 |
|a Machine learning
|v Congresses.
|
| 650 |
|
0 |
|a Diagnostic imaging
|x Data processing
|v Congresses.
|
| 650 |
|
0 |
|a Artificial intelligence
|x Medical applications
|v Congresses.
|
| 650 |
|
7 |
|a Artificial intelligence
|x Medical applications
|2 fast
|
| 650 |
|
7 |
|a Diagnostic imaging
|x Data processing
|2 fast
|
| 650 |
|
7 |
|a Machine learning
|2 fast
|
| 655 |
|
7 |
|a proceedings (reports)
|2 aat
|
| 655 |
|
7 |
|a Conference papers and proceedings
|2 fast
|
| 655 |
|
7 |
|a Conference papers and proceedings.
|2 lcgft
|
| 655 |
|
7 |
|a Actes de congrès.
|2 rvmgf
|
| 700 |
1 |
|
|a Lian, Chunfeng,
|e editor.
|
| 700 |
1 |
|
|a Cao, Xiaohuan,
|e editor.
|
| 700 |
1 |
|
|a Rekik, Islem,
|e editor.
|
| 700 |
1 |
|
|a Xu, Xuanang,
|e editor.
|
| 700 |
1 |
|
|a Yan, Pingkun,
|e editor.
|
| 711 |
2 |
|
|a International Conference on Medical Image Computing and Computer-Assisted Intervention
|n (24th :
|d 2021 :
|c Online)
|
| 758 |
|
|
|i has work:
|a Machine learning in medical imaging (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCGDQfmpdbHrYM38WJpyq8K
|4 https://id.oclc.org/worldcat/ontology/hasWork
|
| 776 |
0 |
8 |
|i Print version:
|a MLMI (Workshop) (12th : 2021 : Online).
|t Machine learning in medical imaging.
|d Cham : Springer, [2021]
|z 3030875881
|z 9783030875886
|w (OCoLC)1264721538
|
| 830 |
|
0 |
|a Lecture notes in computer science ;
|v 12966.
|
| 830 |
|
0 |
|a LNCS sublibrary.
|n SL 6,
|p Image processing, computer vision, pattern recognition, and graphics.
|
| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-030-87589-3
|y Click for online access
|
| 903 |
|
|
|a SPRING-COMP2021
|
| 994 |
|
|
|a 92
|b HCD
|