ArticleMedical image analysis2022
Embracing the disharmony in medical imaging: A Simple and effective framework for domain adaptation.
Article in Medical image analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses that pooled it.
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Who cites it
32 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A meta-analysis and systematic review of single vs. multimodal neuroimaging techniques in the classification of psychosis.Molecular psychiatry · 2023Pooled it
- A systematic overview of dental methods for age assessment in living individuals: from traditional to artificial intelligence-based approaches.International journal of legal medicine · 2023Pooled it
- Precision management of gastrointestinal tumor-associated osteoporosis driven by cutting-edge technologies: Current status, challenges, and future prospects.World journal of methodology · 2026Review
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- Supervised Domain Adaptation Mitigates Cross-Ethnicity Prediction Error in Neuroimaging-Based Cognitive Prediction.bioRxiv : the preprint server for biology · 2026Article
- The impact of scanner domain shift on deep learning performance in medical imaging: an experimental study.International journal of computer assisted radiology and surgery · 2026Article
- AI-Assisted Pneumonia Detection, Localisation and Report Generation from Chest X-rays.medRxiv : the preprint server for health sciences · 2026Article
- From diverse CT scans to generalization: towards robust abdominal organ segmentation.BMC medical imaging · 2026Article
- Understanding pre-training data effects in retinal foundation models using two large fundus cohorts.Nature communications · 2026Article
- Neuroimaging endophenotypes reveal underlying mechanisms and genetic factors contributing to progression and development of four brain disorders.Nature biomedical engineering · 2025Article
- Source-Free Active Learning for Adapting Alzheimer's Diagnostic Deep Learning Models Across Neuroimaging Cohorts.... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics · 2025Article
- AI-powered integration of multimodal imaging in precision medicine for neuropsychiatric disorders.Cell reports. Medicine · 2025Review
- An evaluation of image-based and statistical techniques for harmonizing brain volume measurements.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Generative models of MRI-derived neuroimaging features and associated dataset of 18,000 samples.Scientific data · 2024Article
- A Physics-Informed Deep Neural Network for Harmonization of CT Images.IEEE transactions on bio-medical engineering · 2024Article
- Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 2: recommendations for standardisation, validation, and good clinical practice.The Lancet. Oncology · 2024Review
- The potential of federated learning for self-configuring medical object detection in heterogeneous data distributions.Scientific reports · 2024Article
- Generative models of MRI-derived neuroimaging features and associated dataset of 18,000 samples.ArXiv · 2024Article
- Navigating Clinical Variability: Transfer Learning's Impact on Imaging Model Performance.Radiology. Artificial intelligence · 2024Article
Corrections and comments
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3 authors.
Funding
Abstract
Domain shift, the mismatch between training and testing data characteristics, causes significant degradation in the predictive performance in multi-source imaging scenarios. In medical imaging, the heterogeneity of population, scanners and acquisition protocols at different sites presents a significant domain shift challenge and has limited the widespread clinical adoption of machine learning models. Harmonization methods, which aim to learn a representation of data invariant to these differences are the prevalent tools to address domain shift, but they typically result in degradation of predictive accuracy. This paper takes a different perspective of the problem: we embrace this disharmony in data and design a simple but effective framework for tackling domain shift. The key idea, based on our theoretical arguments, is to build a pretrained classifier on the source data and adapt this model to new data. The classifier can be fine-tuned for intra-study domain adaptation. We can also tackle situations where we do not have access to ground-truth labels on target data; we show how one can use auxiliary tasks for adaptation; these tasks employ covariates such as age, gender and race which are easy to obtain but nevertheless correlated to the main task. We demonstrate substantial improvements in both intra-study domain adaptation and inter-study domain generalization on large-scale real-world 3D brain MRI datasets for classifying Alzheimer's disease and schizophrenia.
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Registered trials
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.