Medical Image Computing and Computer Assisted Intervention MICCAI 2020: 23rd International Conference, Lima, Peru, October 48, 2020, Proceedings, Part I 1st ed. 2020 [Mīkstie vāki]

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.