关于内窥镜图像分析与边缘 AI 迁移学习的预印本。论文在其模拟分析中报告训练时间最多减少 40%；这并非准确率提升 40%，也不证明临床部署或监管批准。
原始文档语言：英语。切换界面语言不会翻译原始文档。
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…打开文档引用此文档
Roble Mumin “Transfer Learning for Medical Imaging”.
2025-03-24.
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current summary qualified; independent peer review not verified