Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part I

Häftad, Engelska, 2020

Av Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz

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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.

Produktinformation

Utgivningsdatum2020-10-02
Mått155 x 235 x 48 mm
Vikt1 317 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor849
FörlagSpringer Nature Switzerland AG
ISBN9783030597092

Tillhör följande kategorier

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.