Iterative Refinement Strategy for Automated Data Labeling: Facial
  Landmark Diagnosis in Medical Imaging
Yu-Hsi Chen
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.