Medical Image Computing and Computer Assisted Intervention - MICCAI 2020 23rd International Conference, Lima, Peru, October 4-8, 2020, Proceedings, Part I / [electronic resource] :
edited by Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz.
- 1st ed. 2020.
- XXXVII, 849 p. 257 illus. online resource.
- Image Processing, Computer Vision, Pattern Recognition, and Graphics, 12261 3004-9954 ; .
- Image Processing, Computer Vision, Pattern Recognition, and Graphics, 12261 .
Machine Learning Methodologies -- Attention, Suggestion and Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation -- Scribble2Label: Scribble-Supervised Cell Segmentation via Self-Generating Pseudo-Labels with Consistency -- Are fast labeling methods reliable? A case study of computer-aided expert annotations on microscopy slides -- Deep Reinforcement Active Learning for Medical Image Classification -- An Effective Data Refinement Approach for Upper Gastrointestinal Anatomy Recognition -- Synthetic Sample Selection via Reinforcement Learning -- Dual-level Selective Transfer Learning for Intrahepatic Cholangiocarcinoma Segmentation in Non-enhanced Abdominal CT -- BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder Architecture -- Constrain Latent Space for Schizophrenia Classification via Dual Space Mapping Net -- Have you forgotten? A method to assess ifmachine learning models have forgotten data -- Learning and Exploiting Interclass Visual Correlations for Medical Image Classification -- Feature Preserving Smoothing Provides Simple and Effective Data Augmentation for Medical Image Segmentation -- Deep kNN for Medical Image Classification -- Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration -- DECAPS: Detail-oriented Capsule Networks -- Federated Simulation for Medical Imaging -- Continual Learning of New Diseases with Dual Distillation and Ensemble Strategy -- Learning to Segment When Experts Disagree -- Deep Disentangled Hashing with Momentum Triplets for Neuroimage Search -- Learning joint shape and appearance representations with metamorphic auto-encoders -- Collaborative Learning of Cross-channel Clinical Attention for Radiotherapy-related Esophageal Fistula Prediction from CT -- Learning Bronchiole-Sensitive Airway Segmentation CNNs by Feature Recalibration and Attention Distillation -- Learning Rich Attention for Pediatric Bone Age Assessment -- Weakly Supervised Organ Localization with Attention Maps Regularized by Local Area Reconstruction -- High-order Attention Networks for Medical Image Segmentation -- NAS-SCAM: Neural Architecture Search-based Spatial and Channel Joint Attention Module for Nuclei Semantic Segmentation and Classification -- Scientific Discovery by Generating Counterfactuals using Image Translation -- Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction -- Encoding Visual Attributes in Capsules for Explainable Medical Diagnoses -- Interpretability-guided Content-based Medical Image Retrieval -- Domain aware medical image classifier interpretation by counterfactual impact analysis -- Towards Emergent Language Symbolic Semantic Segmentation and Model Interpretability -- Meta Corrupted Pixels Mining for Medical Image Segmentation -- UXNet: Searching Multi-level Feature Aggregation for 3D Medical Image Segmentation -- Difficulty-aware Meta-learning for Rare Disease Diagnosis -- Few Is Enough: Task-Augmented Active Meta-Learning for Brain Cell Classification -- Automatic Data Augmentation for 3D Medical Image Segmentation -- MS-NAS: Multi-Scale Neural Architecture Search for Medical Image Segmentation -- Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs By Comparing Image Representations -- Dual-task Self-supervision for Cross-Modality Domain Adaptation -- Dual-Teacher: Integrating Intra-domain and Inter-domain Teachers for Annotation-efficient Cardiac Segmentation -- Test-time Unsupervised Domain Adaptation -- Self domain adapted network -- Entropy Guided Unsupervised Domain Adaptation for Cross-Center Hip Cartilage Segmentation from MRI -- User-Guided Domain Adaptation for Rapid Annotation from User Interactions: A Study on Pathological Liver Segmentation -- SALAD: Self-Supervised Aggregation Learning for Anomaly Detection on X-Rays -- Scribble-based Domain Adaptation via Deep Co-Segmentation -- Source-Relaxed Domain Adaptation for Image Segmentation -- Region-of-interest guided Supervoxel Inpainting for Self-supervision -- Harnessing Uncertainty in Domain Adaptation for MRI Prostate Lesion Segmentation -- Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation -- DMNet: Difference Minimization Network for Semi-supervised Segmentation in Medical Images -- Double-uncertainty Weighted Method for Semi-supervised Learning -- Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images -- Local and Global Structure-aware Entropy Regularized Mean Teacher Model for 3D Left Atrium segmentation -- Improving dense pixelwise prediction of epithelial density using unsupervised data augmentation for consistency regularization -- Knowledge-guided Pretext Learning for Utero-placental Interface Detection -- Self-supervised Depth Estimation to Regularise Semantic Segmentation in Knee Arthroscopy -- Semi-supervised Medical Image Classification with Global Latent Mixing -- Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-Supervised Medical Image Segmentation -- Semi-Supervised Classification of Diagnostic Radiographs with NoTeacher: A Teacher that is not Mean -- Predicting Potential Propensity of Adolescents to Drugs via New Semi-Supervised Deep Ordinal Regression Model -- Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning and Dual-UNet -- Domain Adaptive Relational Reasoning for 3D Multi-Organ Segmentation -- Realistic Adversarial Data Augmentation for MR Image Segmentation -- Learning to Segment Anatomical Structures Accurately from One Exemplar -- Uncertainty estimates as data selection criteria to boost omni-supervised learning -- Extreme Consistency: Overcoming Annotation Scarcity and Domain Shifts -- Spatio-temporal Consistency and Negative LabelTransfer for 3D freehand US Segmentation -- Characterizing Label Errors: Confident Learning for Noisy-labeled Image Segmentation -- Leveraging Undiagnosed Data for Glaucoma Classification with Teacher-Student Learning -- Difficulty-aware Glaucoma Classification with Multi-Rater Consensus Modeling -- Intra-operative Forecasting of Growth Modulation Spine Surgery Outcomes with Spatio-Temporal Dynamic Networks -- Self-supervision on Unlabelled OR Data for Multi-person 2D/3D Human Pose Estimation -- Knowledge distillation from multi-modal to mono-modal segmentation networks -- Heterogeneity Measurement of Cardiac Tissues Leveraging Uncertainty Information from Image Segmentation -- Efficient Shapley Explanation For Features Importance Estimation Under Uncertainty -- Cartilage Segmentation in High-Resolution 3D Micro-CT Images via Uncertainty-Guided Self-Training with Very Sparse Annotation -- Probabilistic 3D surface reconstruction from sparse MRI information -- Can you trust predictive uncertainty under real dataset shifts in digital pathology? -- Deep Generative Model for Synthetic-CT Generation with Uncertainty Predictions.
The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic. 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: machine learning methodologies Part II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis Part IV: segmentation; shape models and landmark detection Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging Part VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography.
9783030597108
10.1007/978-3-030-59710-8 doi
Computer vision.
Artificial intelligence.
Social sciences--Data processing.
Education--Data processing.
Bioinformatics.
Pattern recognition systems.
Computer Vision.
Artificial Intelligence.
Computer Application in Social and Behavioral Sciences.
Computers and Education.
Computational and Systems Biology.
Automated Pattern Recognition.
TA1634
006.37
Machine Learning Methodologies -- Attention, Suggestion and Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation -- Scribble2Label: Scribble-Supervised Cell Segmentation via Self-Generating Pseudo-Labels with Consistency -- Are fast labeling methods reliable? A case study of computer-aided expert annotations on microscopy slides -- Deep Reinforcement Active Learning for Medical Image Classification -- An Effective Data Refinement Approach for Upper Gastrointestinal Anatomy Recognition -- Synthetic Sample Selection via Reinforcement Learning -- Dual-level Selective Transfer Learning for Intrahepatic Cholangiocarcinoma Segmentation in Non-enhanced Abdominal CT -- BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder Architecture -- Constrain Latent Space for Schizophrenia Classification via Dual Space Mapping Net -- Have you forgotten? A method to assess ifmachine learning models have forgotten data -- Learning and Exploiting Interclass Visual Correlations for Medical Image Classification -- Feature Preserving Smoothing Provides Simple and Effective Data Augmentation for Medical Image Segmentation -- Deep kNN for Medical Image Classification -- Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration -- DECAPS: Detail-oriented Capsule Networks -- Federated Simulation for Medical Imaging -- Continual Learning of New Diseases with Dual Distillation and Ensemble Strategy -- Learning to Segment When Experts Disagree -- Deep Disentangled Hashing with Momentum Triplets for Neuroimage Search -- Learning joint shape and appearance representations with metamorphic auto-encoders -- Collaborative Learning of Cross-channel Clinical Attention for Radiotherapy-related Esophageal Fistula Prediction from CT -- Learning Bronchiole-Sensitive Airway Segmentation CNNs by Feature Recalibration and Attention Distillation -- Learning Rich Attention for Pediatric Bone Age Assessment -- Weakly Supervised Organ Localization with Attention Maps Regularized by Local Area Reconstruction -- High-order Attention Networks for Medical Image Segmentation -- NAS-SCAM: Neural Architecture Search-based Spatial and Channel Joint Attention Module for Nuclei Semantic Segmentation and Classification -- Scientific Discovery by Generating Counterfactuals using Image Translation -- Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction -- Encoding Visual Attributes in Capsules for Explainable Medical Diagnoses -- Interpretability-guided Content-based Medical Image Retrieval -- Domain aware medical image classifier interpretation by counterfactual impact analysis -- Towards Emergent Language Symbolic Semantic Segmentation and Model Interpretability -- Meta Corrupted Pixels Mining for Medical Image Segmentation -- UXNet: Searching Multi-level Feature Aggregation for 3D Medical Image Segmentation -- Difficulty-aware Meta-learning for Rare Disease Diagnosis -- Few Is Enough: Task-Augmented Active Meta-Learning for Brain Cell Classification -- Automatic Data Augmentation for 3D Medical Image Segmentation -- MS-NAS: Multi-Scale Neural Architecture Search for Medical Image Segmentation -- Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs By Comparing Image Representations -- Dual-task Self-supervision for Cross-Modality Domain Adaptation -- Dual-Teacher: Integrating Intra-domain and Inter-domain Teachers for Annotation-efficient Cardiac Segmentation -- Test-time Unsupervised Domain Adaptation -- Self domain adapted network -- Entropy Guided Unsupervised Domain Adaptation for Cross-Center Hip Cartilage Segmentation from MRI -- User-Guided Domain Adaptation for Rapid Annotation from User Interactions: A Study on Pathological Liver Segmentation -- SALAD: Self-Supervised Aggregation Learning for Anomaly Detection on X-Rays -- Scribble-based Domain Adaptation via Deep Co-Segmentation -- Source-Relaxed Domain Adaptation for Image Segmentation -- Region-of-interest guided Supervoxel Inpainting for Self-supervision -- Harnessing Uncertainty in Domain Adaptation for MRI Prostate Lesion Segmentation -- Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation -- DMNet: Difference Minimization Network for Semi-supervised Segmentation in Medical Images -- Double-uncertainty Weighted Method for Semi-supervised Learning -- Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images -- Local and Global Structure-aware Entropy Regularized Mean Teacher Model for 3D Left Atrium segmentation -- Improving dense pixelwise prediction of epithelial density using unsupervised data augmentation for consistency regularization -- Knowledge-guided Pretext Learning for Utero-placental Interface Detection -- Self-supervised Depth Estimation to Regularise Semantic Segmentation in Knee Arthroscopy -- Semi-supervised Medical Image Classification with Global Latent Mixing -- Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-Supervised Medical Image Segmentation -- Semi-Supervised Classification of Diagnostic Radiographs with NoTeacher: A Teacher that is not Mean -- Predicting Potential Propensity of Adolescents to Drugs via New Semi-Supervised Deep Ordinal Regression Model -- Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning and Dual-UNet -- Domain Adaptive Relational Reasoning for 3D Multi-Organ Segmentation -- Realistic Adversarial Data Augmentation for MR Image Segmentation -- Learning to Segment Anatomical Structures Accurately from One Exemplar -- Uncertainty estimates as data selection criteria to boost omni-supervised learning -- Extreme Consistency: Overcoming Annotation Scarcity and Domain Shifts -- Spatio-temporal Consistency and Negative LabelTransfer for 3D freehand US Segmentation -- Characterizing Label Errors: Confident Learning for Noisy-labeled Image Segmentation -- Leveraging Undiagnosed Data for Glaucoma Classification with Teacher-Student Learning -- Difficulty-aware Glaucoma Classification with Multi-Rater Consensus Modeling -- Intra-operative Forecasting of Growth Modulation Spine Surgery Outcomes with Spatio-Temporal Dynamic Networks -- Self-supervision on Unlabelled OR Data for Multi-person 2D/3D Human Pose Estimation -- Knowledge distillation from multi-modal to mono-modal segmentation networks -- Heterogeneity Measurement of Cardiac Tissues Leveraging Uncertainty Information from Image Segmentation -- Efficient Shapley Explanation For Features Importance Estimation Under Uncertainty -- Cartilage Segmentation in High-Resolution 3D Micro-CT Images via Uncertainty-Guided Self-Training with Very Sparse Annotation -- Probabilistic 3D surface reconstruction from sparse MRI information -- Can you trust predictive uncertainty under real dataset shifts in digital pathology? -- Deep Generative Model for Synthetic-CT Generation with Uncertainty Predictions.
The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic. 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: machine learning methodologies Part II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis Part IV: segmentation; shape models and landmark detection Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging Part VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography.
9783030597108
10.1007/978-3-030-59710-8 doi
Computer vision.
Artificial intelligence.
Social sciences--Data processing.
Education--Data processing.
Bioinformatics.
Pattern recognition systems.
Computer Vision.
Artificial Intelligence.
Computer Application in Social and Behavioral Sciences.
Computers and Education.
Computational and Systems Biology.
Automated Pattern Recognition.
TA1634
006.37