Medical Image Computing and Computer Assisted Intervention - MICCAI 2020 : 23rd International Conference, Lima, Peru, October 4-8, 2020, Proceedings, Part I / edited by Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz.

Format:

Book

Contributor:

Martel, Anne L., Editor.

Abolmaesumi, Purang, Editor.

Stoyanov, Danail, Editor.

Mateus, Diana., Editor.

Zuluaga, Maria A., Editor.

Zhou, S. Kevin, Editor.

Racoceanu, Daniel, Editor.

Joskowicz, Leo., Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 12261

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 12261

Language:

English

Subjects (All):

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.

Local Subjects:

Computer Vision.

Artificial Intelligence.

Computer Application in Social and Behavioral Sciences.

Computers and Education.

Computational and Systems Biology.

Automated Pattern Recognition.

Physical Description:

1 online resource (XXXVII, 849 pages) : 257 illustrations

Edition:

1st ed. 2020.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2020.

System Details:

text file PDF

Summary:

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.

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-030-59710-8

9783030597108

Access Restriction:

Restricted for use by site license.