Abstract
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.
Abstract (translated)
自动数据标注技术对于加速深度学习模型的开发，尤其是在复杂的医学成像应用中，至关重要。然而，确保准确性和效率仍然具有挑战性。本文介绍了一种用于面部 landmark 诊断的自动数据标注的迭代改进策略，以提高医学应用中深度学习模型的准确性和效率，包括皮肤科、整形外科和眼科。通过利用反馈机制和先进算法，我们的方法迭代地优化初始标签，减少对手动干预的依赖，同时提高标签质量。通过实验评估和案例研究，我们证明了我们在医学成像领域中的深度学习任务的实际效果。我们的结果强调了在自动数据标注中进行迭代改进对于增强深度学习系统在医学成像应用中的能力的重要性。
URL
https://arxiv.org/abs/2404.05348
