Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health : Third MICCAI Workshop, DART 2021, and First MICCAI Workshop, FAIR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27 and October 1, 2021, Proceedings / edited by Shadi Albarqouni, M. Jorge Cardoso, Qi Dou, Konstantinos Kamnitsas, Bishesh Khanal, Islem Rekik, Nicola Rieke, Debdoot Sheet, Sotirios Tsaftaris, Daguang Xu, Ziyue Xu.

Format:

Book

Contributor:

Albarqouni, Shadi, Editor.

Cardoso, M. Jorge, Editor.

Dou, Qi., Editor.

Kamnitsas, Konstantinos., Editor.

Khanal, Bishesh., Editor.

Rekik, Islem, Editor.

Rieke, Nicola., Editor.

Sheet, Debdoot., Editor.

Tsaftaris, Sotirios., Editor.

Xu, Daguang., Editor.

Xu, Ziyue., Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 12968

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 12968

Language:

English

Subjects (All):

Computer vision.

Artificial intelligence.

Bioinformatics.

Medical informatics.

Computer Vision.

Artificial Intelligence.

Computational and Systems Biology.

Health Informatics.

Local Subjects:

Computer Vision.

Artificial Intelligence.

Computational and Systems Biology.

Health Informatics.

Physical Description:

1 online resource (XV, 264 pages) : 95 illustrations, 90 illustrations in color.

Edition:

1st ed. 2021.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2021.

System Details:

text file PDF

Summary:

This book constitutes the refereed proceedings of the Third MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2021, and the First MICCAI Workshop on Affordable Healthcare and AI for Resource Diverse Global Health, FAIR 2021, held in conjunction with MICCAI 2021, in September/October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic. DART 2021 accepted 13 papers from the 21 submissions received. The workshop aims at creating a discussion forum to compare, evaluate, and discuss methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical setting by making them robust and consistent across different domains. For FAIR 2021, 10 papers from 17 submissions were accepted for publication. They focus on Image-to-Image Translation particularly for low-dose or low-resolution settings; Model Compactness and Compression; Domain Adaptation and Transfer Learning; Active, Continual and Meta-Learning. .

Contents:

Domain Adaptation and Representation Transfer

A Systematic Benchmarking Analysis of Transfer Learning for Medical Image Analysis

Self-supervised Multi-scale Consistency for Weakly Supervised Segmentation Learning

FDA: Feature Decomposition and Aggregation for Robust Airway Segmentation

Adversarial Continual Learning for Multi-Domain Hippocampal Segmentation

Self-Supervised Multimodal Generalized Zero Shot Learning For Gleason Grading

Self-Supervised Learning of Inter-Label Geometric Relationships For Gleason Grade Segmentation

Stop Throwing Away Discriminators! Re-using Adversaries for Test-Time Training

Transductive image segmentation: Self-training and effect of uncertainty estimation

Unsupervised Domain Adaptation with Semantic Consistency across Heterogeneous Modalities for MRI Prostate Lesion Segmentation

Cohort Bias Adaptation in Federated Datasets for Lesion Segmentation

Exploring Deep Registration Latent Spaces

Learning from Partially Overlapping Labels: Image Segmentation under Annotation Shift

Unsupervised Domain Adaption via Similarity-based Prototypes for Cross-Modality Segmentation

A ordable AI and Healthcare

Classification and Generation of Microscopy Images with Plasmodium Falciparum via Arti cial Neural Networks using Low Cost Settings

Contrast and Resolution Improvement of POCUS Using Self-Consistent CycleGAN

Low-Dose Dynamic CT Perfusion Denoising without Training Data

Recurrent Brain Graph Mapper for Predicting Time-Dependent Brain Graph Evaluation Trajectory

COVID-Net US: A Tailored, Highly Efficient, Self-Attention Deep Convolutional Neural Network Design for Detection of COVID-19Patient Cases from Point-of-care Ultrasound Imaging

Inter-Domain Alignment for Predicting High-Resolution Brain Networks Using Teacher-Student Learning

Sickle Cell Disease Severity Prediction from Percoll Gradient Images using Graph Convolutional Networks

Continual Domain Incremental Learning for Chest X-ray Classification in Low-Resource Clinical Settings

Deep learning based Automatic detection of adequately positioned mammograms

Can non-specialists provide high quality Gold standard labels in challenging modalities.

Other Format:

Printed edition:

ISBN:

978-3-030-87722-4

9783030877224

Access Restriction:

Restricted for use by site license.