Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health
Springer International Publishing (Verlag)
978-3-030-87721-7 (ISBN)
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.
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 Classificationin 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.
| Erscheinungsdatum | 25.09.2021 |
|---|---|
| Reihe/Serie | Image Processing, Computer Vision, Pattern Recognition, and Graphics | Lecture Notes in Computer Science |
| Zusatzinfo | XV, 264 p. 95 illus., 90 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 433 g |
| Themenwelt | Informatik ► Grafik / Design ► Digitale Bildverarbeitung |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Informatik ► Weitere Themen ► Bioinformatik | |
| Technik | |
| Schlagworte | Applications • Artificial Intelligence • Bioinformatics • color image processing • Computer Science • computer vision • conference proceedings • Deep learning • Image Analysis • Image Processing • image reconstruction • Image Segmentation • Imaging Systems • Informatics • machine learning • Medical Image Analysis • Medical Images • Medical Imaging • Neural networks • pattern recognition • Research • segmentation methods |
| ISBN-10 | 3-030-87721-3 / 3030877213 |
| ISBN-13 | 978-3-030-87721-7 / 9783030877217 |
| Zustand | Neuware |
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