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Transfer Learning for Medical Imaging
A preprint on transfer learning for endoscopic image analysis and edge AI. The paper reports up to 40% lower training time in its described simulation-based analysis; this is not a 40% accuracy gain or evidence of clinical deployment or regulatory approval.
Transfer Learning for Medical Imaging:
Enhancing AI in Endoscopic Diagnosis
Achievements and Future Directions
arXiv Category:
Computer Vision (cs.CV)
Image Processing (eess.IV)
Preprint for:
arXiv.org
Roble Mumin
eMail
LinkedIn Profile
March 24, 2025
Abstract
Transfer learning has become a key enabler in medical image analysis, allowing pre-trained
deep learning models to be adapted for specialized medical tasks while addressing critical
challenges such asdata scarcityandhigh annotation costs. This study investigates its
role in improvingdiagnostic accuracyandcomputational efficiency, with a specific
focus on its application inendoscopic image analysis.
Experimental results demonstrate that transfer learning can reducetraining timeby up
to 40% while enhancingdiagnostic precision, particularly inresource-constrained clin-
ical environments. Additionally, we explore its integration withedge AI architectures
to enable decentralized, real-time decision-making, reducing reliance on cloud-based com-
puting.
Beyond technical advancements, this study evaluates criticalethical and regulatory
considerationsto ensure responsible AI deployment in clinical practice. Compliance
withGDPR, HIPAA, and EU-MDR 2017 is analyzed to establish a framework for
the safe and transparent integration of AI-driven diagnostics.
By contextualizing transfer learning’s role in modern healthcare, this work highlights
its potential to support the next generation ofintelligent, efficient, and…
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Cite this document
Roble Mumin “Transfer Learning for Medical Imaging”.
2025-03-24.
Available: Document landing page
current summary qualified; independent peer review not verified
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