mlsanityData to 14 Oct 2026

Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation

arXiv 1702.07841 ↗First posted 25 Feb 2017cs.CVcited by 66

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Abstract (arXiv)

Magnetic Resonance Imaging (MRI) is widely used in routine clinical diagnosis and treatment. However, variations in MRI acquisition protocols result in different appearances of normal and diseased tissue in the images. Convolutional neural networks (CNNs), which have shown to be successful in many medical image analysis tasks, are typically sensitive to the variations in imaging protocols. Therefore, in many cases, networks trained on data acquired with one MRI protocol, do not perform satisfactorily on data acquired with different protocols. This limits the use of models trained with large annotated legacy datasets on a new dataset with a different domain which is often a recurring situation in clinical settings. In this study, we aim to answer the following central questions regarding domain adaptation in medical image analysis: Given a fitted legacy model, 1) How much data from the new domain is required for a decent adaptation of the original network?; and, 2) What portion of the pre-trained model parameters should be retrained given a certain number of the new domain training samples? To address these questions, we conducted extensive experiments in white matter hyperintensity segmentation task. We trained a CNN on legacy MR images of brain and evaluated the performance of the domain-adapted network on the same task with images from a different domain. We then compared the performance of the model to the surrogate scenarios where either the same trained network is used or a new network is trained from scratch on the new dataset.The domain-adapted network tuned only by two training examples achieved a Dice score of 0.63 substantially outperforming a similar network trained on the same set of examples from scratch.

Key facts. Cited by 66 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 Feb 2017.

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Citing papers per year: 2021: 19; 2022: 13; 2023: 14; 2024: 6; 2025: 8; 2026: 6.

66 papers.

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  1. 2021Domain Adaptation for Medical Image Analysis: A Surveycited by 352
  2. 2021SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retrainingcited by 237
  3. 2023UniverSeg: Universal Medical Image Segmentationcited by 91
  4. 2022NICO++: Towards Better Benchmarking for Domain Generalizationcited by 86
  5. 2022Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysiscited by 63
  6. 2021Source-Free Domain Adaptive Fundus Image Segmentation with Denoised Pseudo-Labelingcited by 61
  7. 2021Applications of Deep Learning in Fundus Images: A Reviewcited by 59
  8. 2022Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasetscited by 51
  9. 2021Domain Generalization on Medical Imaging Classification using Episodic Training with Task Augmentationcited by 33
  10. 2023Each Test Image Deserves A Specific Prompt: Continual Test-Time Adaptation for 2D Medical Image Segmentationcited by 29
  11. 2022DLTTA: Dynamic Learning Rate for Test-time Adaptation on Cross-domain Medical Imagescited by 28
  12. 2023Exploring Transfer Learning in Medical Image Segmentation using Vision-Language Modelscited by 28
38 more
  1. 2022LE-UDA: Label-efficient unsupervised domain adaptation for medical image segmentationcited by 27
  2. 2023Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectivescited by 24
  3. 2023Continual Learning in Medical Image Analysis: A Comprehensive Review of Recent Advancements and Future Prospectscited by 22
  4. 2021Transfer Learning in Magnetic Resonance Brain Imaging: a Systematic Reviewcited by 19
  5. 2021Calibrating the Dice loss to handle neural network overconfidence for biomedical image segmentationcited by 17
  6. 2022Memory Consistent Unsupervised Off-the-Shelf Model Adaptation for Source-Relaxed Medical Image Segmentationcited by 15
  7. 2022CellTranspose: Few-shot Domain Adaptation for Cellular Instance Segmentationcited by 11
  8. 2021Multiple Sclerosis Lesion Analysis in Brain Magnetic Resonance Images: Techniques and Clinical Applicationscited by 10
  9. 2022Reducing Domain Gap in Frequency and Spatial domain for Cross-modality Domain Adaptation on Medical Image Segmentationcited by 10
  10. 2021Anatomy of Domain Shift Impact on U-Net Layers in MRI Segmentationcited by 9
  11. 2021Dual-Teacher++: Exploiting Intra-domain and Inter-domain Knowledge with Reliable Transfer for Cardiac Segmentationcited by 8
  12. 2021Towards Ultrafast MRI via Extreme k-Space Undersampling and Superresolutioncited by 5
  13. 2022Robust Segmentation of Brain MRI in the Wild with Hierarchical CNNs and no Retrainingcited by 5
  14. 2023A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentationcited by 5
  15. 2024CoSAM: Self-Correcting SAM for Domain Generalization in 2D Medical Image Segmentationcited by 5
  16. 2022GearNet: Stepwise Dual Learning for Weakly Supervised Domain Adaptationcited by 4
  17. 2023Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric researchcited by 4
  18. 2023A Client-server Deep Federated Learning for Cross-domain Surgical Image Segmentationcited by 4
  19. 2023A2V: A Semi-Supervised Domain Adaptation Framework for Brain Vessel Segmentation via Two-Phase Training Angiography-to-Venography Translationcited by 4
  20. 2023Investigating a domain adaptation approach for integrating different measurement instruments in a longitudinal clinical registrycited by 3
  21. 2024Few-shot learning for COVID-19 Chest X-Ray Classification with Imbalanced Data: An Inter vs. Intra Domain Studycited by 3
  22. 2021Rethinking Transfer Learning for Medical Image Classificationcited by 2
  23. 2021Targeted Gradient Descent: A Novel Method for Convolutional Neural Networks Fine-tuning and Online-learningcited by 2
  24. 2021Dispensed Transformer Network for Unsupervised Domain Adaptationcited by 2
  25. 2023Brain tumor multi classification and segmentation in MRI images using deep learningcited by 2
  26. 2023ProtoKD: Learning from Extremely Scarce Data for Parasite Ova Recognitioncited by 2
  27. 2024OCT-SelfNet: A Self-Supervised Framework with Multi-Modal Datasets for Generalized and Robust Retinal Disease Detectioncited by 2
  28. 2024DART: A Principled Approach to Adversarially Robust Unsupervised Domain Adaptationcited by 2
  29. 2025Deep Unsupervised Anomaly Detection in Brain Imaging: Large-Scale Benchmarking and Bias Analysiscited by 2
  30. 2021Unsupervised Domain Adaptation Network with Category-Centric Prototype Aligner for Biomedical Image Segmentationcited by 1
  31. 2021RootPainter3D: Interactive-machine-learning enables rapid and accurate contouring for radiotherapycited by 1
  32. 2021Edge-preserving Domain Adaptation for semantic segmentation of Medical Imagescited by 1
  33. 2023Speeding Up EfficientNet: Selecting Update Blocks of Convolutional Neural Networks using Genetic Algorithm in Transfer Learningcited by 1
  34. 2024Domain Adaptation-Enhanced Searchlight: Enabling classification of brain states from visual perception to mental imagerycited by 1
  35. 2025Modality-Agnostic Input Channels Enable Segmentation of Brain lesions in Multimodal MRI with Sequences Unavailable During Trainingcited by 1
  36. 2025Unsupervised Deep Generative Models for Anomaly Detection in Neuroimaging: A Systematic Scoping Reviewcited by 1
  37. 2025Using latent representations to link disjoint longitudinal data for mixed-effects regressioncited by 1
  38. 2026SPEGC: Continual Test-Time Adaptation via Semantic-Prompt-Enhanced Graph Clustering for Medical Image Segmentationcited by 1

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