Automated data labeling techniques are crucial for accelerating the development of deep learning models, particularly in complex medical imaging applications. However, ensuring accuracy and efficiency remains challenging. This paper presents iterative refinement strategies for automated data labeling in facial landmark diagnosis to enhance accuracy and efficiency for deep learning models in medical applications, including dermatology, plastic surgery, and ophthalmology. Leveraging feedback mechanisms and advanced algorithms, our approach iteratively refines initial labels, reducing reliance on manual intervention while improving label quality. Through empirical evaluation and case studies, we demonstrate the effectiveness of our proposed strategies in deep learning tasks across medical imaging domains. Our results highlight the importance of iterative refinement in automated data labeling to enhance the capabilities of deep learning systems in medical imaging applications.

翻译：自动数据标注技术对于加速深度学习模型开发至关重要，尤其在复杂的医疗影像应用中。然而，确保准确性和效率仍面临挑战。本文提出了一种用于面部标志点诊断的迭代式精细化策略，旨在提升医疗应用（包括皮肤科、整形外科和眼科）中深度学习模型的准确性与效率。通过利用反馈机制和先进算法，我们的方法能迭代地优化初始标注，在减少人工干预的同时提升标注质量。通过实证评估和案例研究，我们证明了所提策略在跨医疗影像领域的深度学习任务中的有效性。结果强调了迭代式精细化在自动数据标注中的重要性，以增强医疗影像应用中深度学习系统的能力。