Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 [E-Book] : 24th International Conference, Strasbourg, France, September 27-October 1, 2021, Proceedings, Part III / edited by Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert.
The eight-volume set LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907, and 12908 constitutes the refereed proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.* The 542 r...
Saved in:
Full text |
|
Personal Name(s): | Cattin, Philippe C., editor |
Cotin, Stéphane, editor / Essert, Caroline, editor / Padoy, Nicolas, editor / Speidel, Stefanie, editor / Zheng, Yefeng, editor / de Bruijne, Marleen, editor | |
Edition: |
1st edition 2021. |
Imprint: |
Cham :
Springer,
2021
|
Physical Description: |
XXXVI, 648 pages 200 illustrations, 185 illustrations in color (online resource) |
Note: |
englisch |
ISBN: |
9783030871994 |
DOI: |
10.1007/978-3-030-87199-4 |
Series Title: |
/* Depending on the record driver, $field may either be an array with
"name" and "number" keys or a flat string containing only the series
name. We should account for both cases to maximize compatibility. */?>
Image Processing, Computer Vision, Pattern Recognition, and Graphics ;
12903 /* Depending on the record driver, $field may either be an array with "name" and "number" keys or a flat string containing only the series name. We should account for both cases to maximize compatibility. */?> Lecture Notes in Computer Science |
Subject (LOC): |
LEADER | 09543nam a22005175i 4500 | ||
---|---|---|---|
001 | 978-3-030-87199-4 | ||
003 | Springer | ||
008 | 210923s2021 sz | s |||| 0|eng d | ||
020 | |a 9783030871994 | ||
024 | 7 | |a 10.1007/978-3-030-87199-4 |2 doi | |
035 | |a (Sirsi) a860966 | ||
041 | |a eng | ||
082 | 0 | 4 | |a 006.37 |2 23 |
082 | 0 | 4 | |a 006.6 |2 23 |
245 | 1 | 0 | |a Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 |h [E-Book] : |b 24th International Conference, Strasbourg, France, September 27-October 1, 2021, Proceedings, Part III / |c edited by Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert. |
250 | |a 1st edition 2021. | ||
264 | 1 | |a Cham : |b Springer, |c 2021 |e (Springer LINK) |f SpringerComputerScience20211020 | |
300 | |a XXXVI, 648 pages 200 illustrations, 185 illustrations in color (online resource) | ||
336 | |a text |b txt |2 rdacontent | ||
337 | |a computer |b c |2 rdamedia | ||
338 | |a online resource |b cr |2 rdacarrier | ||
347 | |a text file |b PDF |2 rda | ||
490 | |a Image Processing, Computer Vision, Pattern Recognition, and Graphics ; |v 12903 | ||
490 | |a Lecture Notes in Computer Science | ||
500 | |a englisch | ||
505 | 0 | |a Machine Learning - Advances in Machine Learning Theory -- Towards Robust General Medical Image Segmentation -- Joint Motion Correction and Super Resolution for Cardiac Segmentation via Latent Optimisation -- Targeted Gradient Descent: A Novel Method for Convolutional Neural Networks Fine-tuning and Online-learning -- A Hierarchical Feature Constraint to CamouflageMedical Adversarial Attacks -- Group Shift Pointwise Convolution for Volumetric Medical Image Segmentation -- Machine Learning - Attention models -- UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation -- AlignTransformer: Hierarchical Alignment of Visual Regions and Disease Tags for Medical Report Generation -- Continuous-Time Deep Glioma Growth Models -- Spine-Transformers: Vertebra Detection and Localization in Arbitrary Field-of-View Spine CT with Transformers -- Multi-view analysis of unregistered medical images using cross-view transformers -- Machine Learning - Domain Adaptation -- Stain Mix-up: Unsupervised Domain Generalization for Histopathology Images -- A Unified Hyper-GAN Model for Unpaired Multi-contrast MR Image Translation -- Generative Self-training for Cross-domain Unsupervised Tagged-to-Cine MRI Synthesis -- Cooperative Training and Latent Space Data Augmentation for Robust Medical Image Segmentation -- Controllable cardiac synthesis via disentangled anatomy arithmetic -- CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation -- Harmonization with Flow-based Causal Inference -- Uncertainty-Aware Label Rectification for Domain Adaptive Mitochondria Segmentation -- Semantic Consistent Unsupervised Domain Adaptation for Cross-modality Medical Image Segmentation -- Anatomy of Domain Shift Impact on U-Net Layers in MRI Segmentation -- FoldIt: Haustral Folds Detection and Segmentation in Colonoscopy Videos -- Reference-Relation Guided Autoencoder with Deep CCA Restriction for Awake-to-Sleep Brain Functional Connectome Prediction -- Domain Composition and Attention for Unseen-Domain Generalizable Medical Image Segmentation -- Fully Test-time Adaptation for Image Segmentation -- OLVA: Optimal Latent Vector Alignment for Unsupervised Domain Adaptation in Medical Image Segmentation -- Prototypical Interaction Graph for Unsupervised Domain Adaptation in Surgical Instrument Segmentation -- Unsupervised Domain Adaptation for Small Bowel Segmentation using Disentangled Representation -- Data-driven mapping between functional connectomes using optimal transport -- EndoUDA: A modality independent segmentation approach for endoscopy imaging -- Style Transfer Using Generative Adversarial Networks for Multi-Site MRI Harmonization -- Machine Learning - Federated Learning -- Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching -- FedPerl: Semi-Supervised Peer Learning for Skin Lesion Classification -- Personalized Retrogress-Resilient Framework for Real-World Medical Federated Learning -- Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures -- Federated Contrastive Learning for Volumetric Medical Image Segmentation -- Federated Contrastive Learning for Decentralized Unlabeled Medical Images -- Machine Learning - Interpretability / Explainability -- Explaining COVID-19 and Thoracic Pathology Model Predictions by Identifying Informative Input Features -- Demystifying T1-MRI to FDG18-PET Image Translation via Representational Similarity -- Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation -- An Interpretable Approach to Automated Severity Scoring in Pelvic Trauma -- Scalable, Axiomatic Explanations of Deep Alzheimer's Diagnosis from Heterogeneous Data -- SPARTA: An Integrated Stability, Discriminability, and Sparsity based Radiomic Feature Selection Approach -- The Power of Proxy Data and Proxy Networks for Hyper-Parameter Optimization for Medical Image Segmentation -- Fighting Class Imbalance with Contrastive Learning -- Interpretable gender classification from retinal fundus images using BagNets -- Explainable Classification of Weakly Annotated Wireless Capsule Endoscopy Images based on a Fuzzy Bag-of-Colour Features Model and Brain Storm Optimization -- Towards Semantic Interpretation of Thoracic Disease and COVID-19 Diagnosis Models -- A Principled Approach to Failure Analysis and Model Repairment: Demonstration in Medical Imaging -- Using Causal Analysis for Conceptual Deep Learning Explanation -- A spherical convolutional neural network for white matter structure imaging via diffusion MRI -- Sharpening Local Interpretable Model-agnostic Explanations for Histopathology: Improved Understandability and Reliability -- Improving the Explainability of Skin Cancer Diagnosis Using CBIR -- PAC Bayesian Performance Guarantees for (Stochastic) Deep Networks in Medical Imaging -- Machine Learning - Uncertainty -- Medical Matting: A New Perspective on Medical Segmentation with Uncertainty -- Confidence-aware Cascaded Network for Fetal Brain Segmentation on MR Images -- Orthogonal Ensemble Networks for Biomedical Image Segmentation -- Learning to Predict Error for MRI Reconstruction -- Uncertainty-Guided Progressive GANs for Medical Image Translation -- Variational Topic Inference for Chest X-Ray Report Generation -- Uncertainty Aware Deep Reinforcement Learning for Anatomical Landmark Detection in Medical Images. | |
520 | |a The eight-volume set LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907, and 12908 constitutes the refereed proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.* The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: image segmentation Part II: machine learning - self-supervised learning; machine learning - semi-supervised learning; and machine learning - weakly supervised learning Part III: machine learning - advances in machine learning theory; machine learning - domain adaptation; machine learning - federated learning; machine learning - interpretability / explainability; and machine learning - uncertainty Part IV: image registration; image-guided interventions and surgery; surgical data science; surgical planning and simulation; surgical skill and work flow analysis; and surgical visualization and mixed, augmented and virtual reality Part V: computer aided diagnosis; integration of imaging with non-imaging biomarkers; and outcome/disease prediction Part VI: image reconstruction; clinical applications - cardiac; and clinical applications - vascular Part VII: clinical applications - abdomen; clinical applications - breast; clinical applications - dermatology; clinical applications - fetal imaging; clinical applications - lung; clinical applications - neuroimaging - brain development; clinical applications - neuroimaging - DWI and tractography; clinical applications - neuroimaging - functional brain networks; clinical applications - neuroimaging - others; and clinical applications - oncology Part VIII: clinical applications - ophthalmology; computational (integrative) pathology; modalities - microscopy; modalities - histopathology; and modalities - ultrasound *The conference was held virtually. | ||
650 | 0 | |a Artificial intelligence. | |
650 | 0 | |a Bioinformatics. | |
650 | 0 | |a Health informatics. | |
650 | 0 | |a Optical data processing. | |
650 | 0 | |a Pattern recognition. | |
700 | 1 | |a Cattin, Philippe C., |e editor | |
700 | 1 | |a Cotin, Stéphane, |e editor | |
700 | 1 | |a Essert, Caroline, |e editor | |
700 | 1 | |a Padoy, Nicolas, |e editor | |
700 | 1 | |a Speidel, Stefanie, |e editor | |
700 | 1 | |a Zheng, Yefeng, |e editor | |
700 | 1 | |a de Bruijne, Marleen, |e editor | |
856 | 4 | 0 | |u https://doi.org/10.1007/978-3-030-87199-4 |z Volltext |
908 | |a Konferenz | ||
915 | |a zzwFZJ3 | ||
932 | |a Computer Science (R0) (SpringerNature-43710) | ||
932 | |a Computer Science (SpringerNature-11645) | ||
596 | |a 1 | ||
949 | |a XX(860966.1) |w AUTO |c 1 |i 860966-1001 |l ELECTRONIC |m ZB |r N |s Y |t E-BOOK |u 20/10/2021 |x ZB-D |z UNKNOWN |0 NEL |1 ONLINE |2 KONFERENZ |