ReviewYearbook of medical informatics2020
Advancing Medical Imaging Informatics by Deep Learning-Based Domain Adaptation.
Review in Yearbook of medical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Assessment of Early Breast Cancer Response to Chemotherapy with Ultrasound Radiomics.Diagnostics (Basel, Switzerland) · 2026Article
- Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine.Psychoradiology · 2026Review
- VAE deep learning model with domain adaptation, transfer learning and harmonization for diagnostic classification from multi-site neuroimaging data.Frontiers in neuroinformatics · 2025Article
- GPT-Driven Radiology Report Generation with Fine-Tuned Llama 3.Bioengineering (Basel, Switzerland) · 2024Article
- Streamlining neuroradiology workflow with AI for improved cerebrovascular structure monitoring.Scientific reports · 2024Article
- Innovative Strategies for Early Autism Diagnosis: Active Learning and Domain Adaptation Optimization.Diagnostics (Basel, Switzerland) · 2024Article
- The Advent of Domain Adaptation into Artificial Intelligence for Gastrointestinal Endoscopy and Medical Imaging.Diagnostics (Basel, Switzerland) · 2023Review
- Using domain adaptation for classification of healthy and disease conditions from mobile-captured images of standard 12-lead electrocardiograms.Scientific reports · 2023Article
- Prediction of cervical lymph node metastasis from immunostained specimens of tongue cancer using a multilayer perceptron neural network.Cancer medicine · 2023Article
- Adaptation to CT Reconstruction Kernels by Enforcing Cross-Domain Feature Maps Consistency.Journal of imaging · 2022Article
- The RETA Benchmark for Retinal Vascular Tree Analysis.Scientific data · 2022Article
- Segmentation of Breast Tubules in H&E Images Based on a DKS-DoubleU-Net Model.BioMed research international · 2022Article
- Domain Adaptation Using Convolutional Autoencoder and Gradient Boosting for Adverse Events Prediction in the Intensive Care Unit.Frontiers in artificial intelligence · 2022Article
- Impact of COVID-19 pandemic on radiology education, training, and practice: A narrative review.World journal of radiology · 2021Review
- Correcting data imbalance for semi-supervised COVID-19 detection using X-ray chest images.Applied soft computing · 2021Article
- Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging.Nature communications · 2021Article
- Application of Artificial Intelligence in COVID-19 Diagnosis and Therapeutics.Journal of personalized medicine · 2021Review
- Notable Papers and New Directions in Sensors, Signals, and Imaging Informatics.Yearbook of medical informatics · 2021Review
- COVID-19 Automatic Diagnosis With Radiographic Imaging: Explainable Attention Transfer Deep Neural Networks.IEEE journal of biomedical and health informatics · 2021Article
- Deep-Learning-Based Color Doppler Ultrasound Image Feature in the Diagnosis of Elderly Patients with Chronic Heart Failure Complicated with Sarcopenia.Journal of healthcare engineering · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThere has been a rapid development of deep learning (DL) models for medical imaging. However, DL requires a large labeled dataset for training the models. Getting large-scale labeled data remains a challenge, and multi-center datasets suffer from heterogeneity due to patient diversity and varying imaging protocols. Domain adaptation (DA) has been developed to transfer the knowledge from a labeled data domain to a related but unlabeled domain in either image space or feature space. DA is a type of transfer learning (TL) that can improve the performance of models when applied to multiple different datasets.
objectiveIn this survey, we review the state-of-the-art DL-based DA methods for medical imaging. We aim to summarize recent advances, highlighting the motivation, challenges, and opportunities, and to discuss promising directions for future work in DA for medical imaging.
methodsWe surveyed peer-reviewed publications from leading biomedical journals and conferences between 2017-2020, that reported the use of DA in medical imaging applications, grouping them by methodology, image modality, and learning scenarios.
resultsWe mainly focused on pathology and radiology as application areas. Among various DA approaches, we discussed domain transformation (DT) and latent feature-space transformation (LFST). We highlighted the role of unsupervised DA in image segmentation and described opportunities for future development.
conclusionDA has emerged as a promising solution to deal with the lack of annotated training data. Using adversarial techniques, unsupervised DA has achieved good performance, especially for segmentation tasks. Opportunities include domain transferability, multi-modal DA, and applications that benefit from synthetic data.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.