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ARXIV:2604.00397 · MEDICAL IMAGING AI · SUBMITTED 02 APR · 21:01 UTC · FRESHNESS STALE
ARXIV:2604.00397MEDICAL IMAGING AISUBMITTED 02 APR · 21:01 UTCFRESHNESS STALEYuchen Yang · Shuangyang Zhong · Haijun Yu · Langcuomu Suo · Hongbin Han · Florian Putz · +1 at arXiv
A domain adaptation framework for brain metastases segmentation across institutions using VAE-MMD and nnU-Net.
Opportunity summary
Pain A domain adaptation framework for brain metastases segmentation across institutions using VAE-MMD and nnU-Net.
Evidence 46 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A domain adaptation framework for brain metastases segmentation across institutions using VAE-MMD and nnU-Net. The goal of this work is to create a domain adaptation framework that will allow for BM segmentation to be…
Background: Deep learning has demonstrated significant potential for automated brain metastases (BM) segmentation; however, models trained at a singular institution often exhibit suboptimal performance at various sites due to disparities in scanner hardware, imaging…
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Results: VAE-MMD reduced domain classifier accuracy from 0.91 to 0.50, indicating successful feature alignment across institutions. Code availability is flagged in the production record;…
Medical Imaging AI moved forward this cycle; last verified April 2026. Public score 6.0/10. Production flags indicate code availability.
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A domain adaptation framework for brain metastases segmentation across institutions using VAE-MMD and nnU-Net.
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10.48550/arXiv.2604.00397A domain adaptation framework for brain metastases segmentation across institutions using VAE-MMD and nnU-Net.
Abstract
Background: Deep learning has demonstrated significant potential for automated brain metastases (BM) segmentation; however, models trained at a singular institution often exhibit suboptimal performance at various sites due to disparities in scanner hardware, imaging protocols, and patient demographics. The goal of this work is to create a domain adaptation framework that will allow for BM segmentation to be used across multiple institutions. Methods: We propose a VAE-MMD preprocessing pipeline that combines variational autoencoders (VAE) with maximum mean discrepancy (MMD) loss, incorporating skip connections and self-attention mechanisms alongside nnU-Net segmentation. The method was tested on 740 patients from four public databases: Stanford, UCSF, UCLM, and PKG, evaluated by domain classifier's accuracy, sensitivity, precision, F1/F2 scores, surface Dice (sDice), and 95th percentile Hausdorff distance (HD95). Results: VAE-MMD reduced domain classifier accuracy from 0.91 to 0.50, indicating successful feature alignment across institutions. Reconstructed volumes attained a PSNR greater than 36 dB, maintaining anatomical accuracy. The combined method raised the mean F1 by 11.1% (0.700 to 0.778), the mean sDice by 7.93% (0.7121 to 0.7686), and reduced the mean HD95 by 65.5% (11.33 to 3.91 mm) across all four centers compared to the baseline nnU-Net. Conclusions: VAE-MMD effectively diminishes cross-institutional data heterogeneity and enhances BM segmentation generalization across volumetric, detection, and boundary-level metrics without necessitating target-domain labels, thereby overcoming a significant obstacle to the clinical implementation of AI-assisted segmentation.
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unverified46 refs; 3 sources; 50% coverage.
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Dimensions overall score 6.0
PROBLEM
A domain adaptation framework for brain metastases segmentation across institutions using VAE-MMD and nnU-Net. The goal of this work is to create a domain adaptation framework that will allow for BM segmentation to be used across multiple institutions.
METHOD
Background: Deep learning has demonstrated significant potential for automated brain metastases (BM) segmentation; however, models trained at a singular institution often exhibit suboptimal performance at various sites due to disparities in scanner hardware, imaging protocols, a...
RESULT
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Results: VAE-MMD reduced domain classifier accuracy from 0.91 to 0.50, indicating successful feature alignment across institutions. Code availability is flagged in the production record; the public reposi...
WHY NOW
Medical Imaging AI moved forward this cycle; last verified April 2026. Public score 6.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A domain adaptation framework for brain metastases segmentation across institutions using VAE-MMD and nnU-Net. The goal of this work is to create a domain adaptation framework that will allow for BM segmentation to be used across multiple institutions.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Background: Deep learning has demonstrated significant potential for automated brain metastases (BM) segmentation; however, models trained at a singular institution often exhibit suboptimal performance at various sites due to disparities in scanner hardware, imaging protocols, and patient demographics. The goal of this work is to create a domain adaptation framework that will allow for BM segmentation to be used across multiple institutions.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Results: VAE-MMD reduced domain classifier accuracy from 0.91 to 0.50, indicating successful feature alignment across institutions. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Medical Imaging AI moved forward this cycle; last verified April 2026. Public score 6.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A domain adaptation framework for brain metastases segmentation across institutions using VAE-MMD and nnU-Net.
Segment
Medical Imaging AI
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6.0/10 public viability
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missing
reason
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proof status
unverified
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confidence low
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Evidence coverage
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46 refs / 3 sources / 50% coverage
stale
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Build readiness
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passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
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partial
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Evidence
46 references, 3 sources, 50% evidence coverage.
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Write integration checklist from prototype path and target workflow.
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DEFENSIBILITY
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