Iterative Refinement Strategy for Automated Data Labeling: Facial Landmark Diagnosis in Medical Imaging

arXiv:2404.05348v1 Announce Type: cross
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 …

abstract accuracy applications arxiv automated automated data labeling cs.ai cs.cv cs.lg data data labeling deep learning development diagnosis efficiency however imaging iterative labeling landmark medical medical imaging paper strategies strategy type