Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-16T00:22:51.762169Z
Paper Citation Record · LEDGER
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging
As of 19 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 0 inbound Pith citation observations for arXiv:2608.12035.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
55 of 55 outbound references displayed
- verified exact4
- verified fuzzy39
- unresolved11
- parse uncertain0
- malformed identifier1
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation f044ccab-51c8-4349-a066-7c9ff7799e15 · outbound
This paper cites an unresolved cited work.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Unresolved cited work
Reference 1
- Resolution
- unresolved
- raw_fallback, observed 2026-08-16T00:22:52.599471Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation be5072b3-37ee-4dd1-9958-6c97c190b78d · outbound
This paper cites an unresolved cited work.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Unresolved cited work
Reference 2
- Resolution
- unresolved
- raw_fallback, observed 2026-08-16T00:22:52.586827Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation e62fd7d9-6afe-422a-933e-f905c90f1574 · outbound
This paper cites an unresolved cited work.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Unresolved cited work
Reference 3
- Resolution
- unresolved
- raw_fallback, observed 2026-08-16T00:22:52.572221Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation dd5665d8-65ce-4392-9889-5db2811013b8 · outbound
This paper cites Reusing the task-specific classifier as a discriminator: Discriminator-free adversarial domain adaptation.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Reusing the task-specific classifier as a discriminator: Discriminator-free adversarial domain adaptation
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.559879Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 3cdcb344-3ad2-442c-b983-c8b24b52f233 · outbound
This paper cites Advancing medical imaging informatics by deep learning-based domain adaptation.Yearbook of medical informatics, 29(01):129– 138, 2020.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Advancing medical imaging informatics by deep learning-based domain adaptation.Yearbook of medical informatics, 29(01):129– 138, 2020
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.547054Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation f7cf5f98-388c-467d-8f1a-ff4470627618 · outbound
This paper cites Towards discriminability and diversity: Batch nuclear-norm maxi- mization under label insufficient situations.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Towards discriminability and diversity: Batch nuclear-norm maxi- mization under label insufficient situations
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.534968Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 97083f4f-2dd1-4b11-adbc-5bb03336f761 · outbound
This paper cites Imagenet: A large-scale hierarchical image database.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Imagenet: A large-scale hierarchical image database
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T00:22:51.574138Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1fa87237-3c77-497d-81c2-5a4c271e27c4 · outbound
This paper cites Cross- moda 2021 challenge: Benchmark of cross-modality do- main adaptation techniques for vestibular schwannoma and cochlea segmentation.Medical Image Analysis, 83:102628,.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Cross- moda 2021 challenge: Benchmark of cross-modality do- main adaptation techniques for vestibular schwannoma and cochlea segmentation.Medical Image Analysis, 83:102628,
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.513292Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation f948ef42-6a05-4c4e-9fbe-1ebd34bdb372 · outbound
This paper cites an unresolved cited work.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Unresolved cited work
Reference 9
- Resolution
- unresolved
- raw_fallback, observed 2026-08-16T00:22:52.500675Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 1aae00f4-2c44-49f6-965c-dcc931b0a326 · outbound
This paper cites Better prac- tices for domain adaptation.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Better prac- tices for domain adaptation
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.486870Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 1718b75e-3318-46e8-ab72-fc5f31696887 · outbound
This paper cites Contrastive domain adaptation with consistency match for automated pneumonia diagnosis.Medical Image Analysis, 83:102664, 2023.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Contrastive domain adaptation with consistency match for automated pneumonia diagnosis.Medical Image Analysis, 83:102664, 2023
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.474965Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation b65d1a4e-7adf-420f-bc87-2fadd342824d · outbound
This paper cites Unsupervised domain adaptation by backpropagation.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Unsupervised domain adaptation by backpropagation
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.462562Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 211dab7b-71ec-4bea-89f4-2279b7e46f7e · outbound
This paper cites Domain-adversarial training of neural networks.Journal of machine learning research, 17(59):1–35, 2016.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Domain-adversarial training of neural networks.Journal of machine learning research, 17(59):1–35, 2016
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.449424Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 3524a89e-94f1-475c-b9e8-0fa1b7fd5626 · outbound
This paper cites Domain adaptation for medical image analysis: a survey.IEEE Transactions on Biomedical Engineering, 69(3):1173–1185, 2021.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Domain adaptation for medical image analysis: a survey.IEEE Transactions on Biomedical Engineering, 69(3):1173–1185, 2021
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.435999Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation a9a6ebfb-5600-4c1f-b57b-4ae2c5fd700d · outbound
This paper cites Multi-site mri har- monization via attention-guided deep domain adaptation for brain disorder identification.Medical image analysis, 71: 102076, 2021.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Multi-site mri har- monization via attention-guided deep domain adaptation for brain disorder identification.Medical image analysis, 71: 102076, 2021
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.422213Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation f7c4c7f5-ec35-4b65-a744-4b0371b353b0 · outbound
This paper cites Deep residual learning for image recognition.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Deep residual learning for image recognition
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.409675Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation ebb330eb-50d6-4273-9ff3-ce56b73373b2 · outbound
This paper cites Mixed samples as probes for un- supervised model selection in domain adaptation.Advances in Neural Information Processing Systems, 36:37923–37941,.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Mixed samples as probes for un- supervised model selection in domain adaptation.Advances in Neural Information Processing Systems, 36:37923–37941,
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.397735Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation ee74871d-70cc-4afd-b63b-49ddf278cc45 · outbound
This paper cites Towards reli- able model selection for unsupervised domain adaptation: An empirical study and a certified baseline.NeurIPS, 37: 135883–135903, 2024.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Towards reli- able model selection for unsupervised domain adaptation: An empirical study and a certified baseline.NeurIPS, 37: 135883–135903, 2024
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.386247Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 4bf93b1a-39f0-4d33-9c1e-c14abe77072e · outbound
This paper cites an unresolved cited work.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Unresolved cited work
Reference 19
- Resolution
- unresolved
- raw_fallback, observed 2026-08-16T00:22:52.373983Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 7f5d8ff3-9d4a-4a4c-93d5-eb109e50dce6 · outbound
This paper cites Minimum class confusion for versatile domain adaptation.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Minimum class confusion for versatile domain adaptation
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.360696Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 10077041-fb15-48e7-8882-0b7dc41b7a32 · outbound
This paper cites Consensus-driven active model selection.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Consensus-driven active model selection
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.349332Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation dfb1fd8d-412d-42e0-89f5-4d1ced1fbf46 · outbound
This paper cites Identifying medical diagnoses and treatable diseases by image-based deep learning.cell, 172(5):1122–1131, 2018.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Identifying medical diagnoses and treatable diseases by image-based deep learning.cell, 172(5):1122–1131, 2018
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.337435Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation da8cb670-d76e-49b0-85ae-ccce8c44b646 · outbound
This paper cites Deep learning for un- supervised domain adaptation in medical imaging: Recent advancements and future perspectives.Computers in Biol- ogy and Medicine, 170:107912, 2024.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Deep learning for un- supervised domain adaptation in medical imaging: Recent advancements and future perspectives.Computers in Biol- ogy and Medicine, 170:107912, 2024
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.325215Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 621050e8-3234-4aa2-8ebd-02ca0e492b74 · outbound
This paper cites Skada-bench: Benchmarking unsupervised domain adapta- tion methods with realistic validation on diverse modalities.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Skada-bench: Benchmarking unsupervised domain adapta- tion methods with realistic validation on diverse modalities
Reference 24
- Resolution
- verified exact
- raw_fallback, observed 2026-08-16T00:22:52.023534Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 9fec6ee6-2801-45fb-b910-57113c38d221 · outbound
This paper cites Medical Image Segmentation with Domain Adaptation: A Survey.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Medical Image Segmentation with Domain Adaptation: A Survey
Reference 25
- Resolution
- verified exact
- local_arxiv, observed 2026-08-16T00:22:51.879145Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 9aa5c579-d02d-4fbd-b039-362d4f0b5dca · outbound
This paper cites Domain adaptation with auxiliary target domain-oriented classifier.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Domain adaptation with auxiliary target domain-oriented classifier
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.313304Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 33f66688-4741-4528-925c-72550f9f8ce9 · outbound
This paper cites Hasd: hierarchical adaption for pathol- ogy slide-level domain-shift.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Hasd: hierarchical adaption for pathol- ogy slide-level domain-shift
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.302093Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation b3d64daa-6760-4d3d-83ab-19f3605e9840 · outbound
This paper cites Attention-guided partial domain adaptation for automated pneumonia diagnosis from chest x-ray images.IEEE Journal of Biomedical and Health Informatics, 27(12):5848–5859,.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Attention-guided partial domain adaptation for automated pneumonia diagnosis from chest x-ray images.IEEE Journal of Biomedical and Health Informatics, 27(12):5848–5859,
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.290869Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 9941a2a7-7609-4064-a190-a9865341df68 · outbound
This paper cites A convnet for the 2020s.CVPR, 2022.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging A convnet for the 2020s.CVPR, 2022
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.278826Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 5df44797-fb5c-41ff-9d80-f4c7d62474d3 · outbound
This paper cites Learn- ing transferable features with deep adaptation networks.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Learn- ing transferable features with deep adaptation networks
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.266588Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 2c829283-d59f-4b97-8bcf-fb210a0ceb35 · outbound
This paper cites Con- ditional adversarial domain adaptation.NeurIPS, 31, 2018.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Con- ditional adversarial domain adaptation.NeurIPS, 31, 2018
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.254927Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 22943838-be38-4567-8967-c8413bc6e5fd · outbound
This paper cites Active model selection: A variance minimization approach.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Active model selection: A variance minimization approach
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.243462Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 7bf04be2-7448-4d23-8a1c-11dcff70a2f8 · outbound
This paper cites Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T00:22:51.676665Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 789050b6-52e4-4338-8b4a-50c7c96ed55e · outbound
This paper cites Unsupervised Domain Adaptation: A Reality Check.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Unsupervised Domain Adaptation: A Reality Check
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T00:22:51.681501Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5c1f8c39-a90a-46a2-881e-0b92d1a2ca69 · outbound
This paper cites Three New Validators and a Large-Scale Benchmark Ranking for Unsupervised Domain Adaptation.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Three New Validators and a Large-Scale Benchmark Ranking for Unsupervised Domain Adaptation
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T00:22:51.685696Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1b75ebe9-2848-4262-96ea-251126b78caa · outbound
This paper cites M3-uda: A new benchmark for unsupervised domain adaptive fetal cardiac structure detection.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging M3-uda: A new benchmark for unsupervised domain adaptive fetal cardiac structure detection
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.231496Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation b433c12e-a15d-4d03-b3e5-0ba1e47285e5 · outbound
This paper cites Maximum classifier discrepancy for unsupervised domain adaptation.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Maximum classifier discrepancy for unsupervised domain adaptation
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.218716Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 02981653-18eb-465a-aeb1-d36970b6bc89 · outbound
This paper cites Tune it the right way: Unsupervised validation of domain adaptation via soft neighborhood density.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Tune it the right way: Unsupervised validation of domain adaptation via soft neighborhood density
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.205622Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation df7af575-885f-4ccc-8da8-a7f1a60e30a6 · outbound
This paper cites Domain Adaptation and Generalization on Functional Medical Images: A Systematic Survey.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Domain Adaptation and Generalization on Functional Medical Images: A Systematic Survey
Reference 39
- Resolution
- verified exact
- local_arxiv, observed 2026-08-16T00:22:51.830383Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 3a243b4e-0365-4ada-b425-d9b4e4633e98 · outbound
This paper cites Active comparison of prediction models.Advances in neural information processing systems, 25, 2012.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Active comparison of prediction models.Advances in neural information processing systems, 25, 2012
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.191956Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 92a2da9b-045f-4bb7-b65b-1efed9519ec9 · outbound
This paper cites M3DA: Benchmark for Unsupervised Domain Adaptation in 3D Medical Image Segmentation.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging M3DA: Benchmark for Unsupervised Domain Adaptation in 3D Medical Image Segmentation
Reference 41
- Resolution
- verified exact
- local_arxiv, observed 2026-08-16T00:22:51.813573Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 6613e358-ff03-4dc5-bded-893c1f761ce2 · outbound
This paper cites Navigating Distribution Shifts in Medical Image Analysis: A Survey.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Navigating Distribution Shifts in Medical Image Analysis: A Survey
Reference 42
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T00:22:51.713462Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ce925f28-c979-4399-8d0c-7dc0b947504f · outbound
This paper cites Covariate shift adaptation by importance weighted cross validation.JMLR, 8(5), 2007.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Covariate shift adaptation by importance weighted cross validation.JMLR, 8(5), 2007
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.178885Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation dee47d67-5734-457f-856e-69614adf4905 · outbound
This paper cites Domain adap- tation for skin lesion: Evaluating real-world generalisation.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Domain adap- tation for skin lesion: Evaluating real-world generalisation
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.166065Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 13f5819c-a837-459f-a5d0-0f2ef2fa4bea · outbound
This paper cites Fairdomain: Achieving fairness in cross-domain medical image segmentation and classifica- tion.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Fairdomain: Achieving fairness in cross-domain medical image segmentation and classifica- tion
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.153139Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 9b7533fc-dc16-4694-8e45-da4edc0f6ddd · outbound
This paper cites Training data-efficient image transformers & distillation through at- tention.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Training data-efficient image transformers & distillation through at- tention
Reference 46
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T00:22:51.729135Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a10f8bbd-5994-4043-b912-860583683f25 · outbound
This paper cites Resmlp: Feedforward networks for image classification with data-efficient training.IEEE TPAMI, 45(4):5314–5321,.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Resmlp: Feedforward networks for image classification with data-efficient training.IEEE TPAMI, 45(4):5314–5321,
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.133876Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 386c77eb-5e15-4aee-a171-19e6fca79974 · outbound
This paper cites Assessing model out-of-distribution generalization with softmax pre- diction probability baselines and a correlation method.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Assessing model out-of-distribution generalization with softmax pre- diction probability baselines and a correlation method
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.122582Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation dad54c25-ee8c-4a39-ab1f-ed143740be8d · outbound
This paper cites Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.110579Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 662ce8c3-a347-49d7-ad55-4d7d59f2fa40 · outbound
This paper cites Unsu- pervised domain adaptation for medical image segmentation by disentanglement learning and self-training.IEEE Trans- actions on Medical Imaging, 43(1):4–14, 2022.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Unsu- pervised domain adaptation for medical image segmentation by disentanglement learning and self-training.IEEE Trans- actions on Medical Imaging, 43(1):4–14, 2022
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.098376Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 90db6920-6d83-4b16-be81-ff7f3a900095 · outbound
This paper cites A survey on unsupervised domain adaptation in medical imaging: Meth- ods, dataset, and future outlook.Applied Soft Computing, page 115314, 2026.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging A survey on unsupervised domain adaptation in medical imaging: Meth- ods, dataset, and future outlook.Applied Soft Computing, page 115314, 2026
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.086709Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 34f789c2-a95d-4a08-b2ff-4699ad3cd793 · outbound
This paper cites Can we evaluate domain adaptation models without target-domain labels? InInternational Conference on Learn- ing Representations, pages 35061–35081, 2024.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Can we evaluate domain adaptation models without target-domain labels? InInternational Conference on Learn- ing Representations, pages 35061–35081, 2024
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.072666Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 53e69fa3-f3c6-44dc-9bb0-2727b961ecc4 · outbound
This paper cites Towards accurate model selection in deep unsupervised domain adap- tation.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Towards accurate model selection in deep unsupervised domain adap- tation
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.059898Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 2cb5a390-ed61-4836-aacd-e8e3500feaef · outbound
This paper cites Collaborative unsupervised domain adaptation for medical image diagno- sis.IEEE TIP, 29:7834–7844, 2020.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging Collaborative unsupervised domain adaptation for medical image diagno- sis.IEEE TIP, 29:7834–7844, 2020
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T00:22:52.047484Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 8ff3f0be-935f-4eb7-9a16-27103523b580 · outbound
This paper cites In addition, we provide the full within-algorithm validator reliability analyses, reported per algorithm and per scenario, in Section 6.2.
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging In addition, we provide the full within-algorithm validator reliability analyses, reported per algorithm and per scenario, in Section 6.2
Reference 55
- Resolution
- malformed identifier
- raw_fallback, observed 2026-08-16T00:22:52.035476Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Pith citing papers
No inbound Pith citation observations are available.