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

Yu-Hsi Chen

TL;DR

The study tackles the accuracy and efficiency of automated facial landmark labeling in medical imaging by introducing an iterative refinement strategy that leverages predictions from prior iterations, a confidence threshold of $0.7$, and NMS filtering at a threshold of $0.3$. The approach progressively expands labeled data across multiple facial landmark datasets (e.g., 300W, AFW, HELEN, IBUG, IFPW) and demonstrates measurable changes in labeling counts and performance metrics. Empirical evaluation using the YOLOv8n-pose framework across varied clinical contexts shows how precision, recall, AP, and MSE evolve through iterations, underscoring the method's potential to accelerate model training while maintaining label quality. The work argues for broader applicability to other medical landmarking tasks and highlights practical benefits for rapid, scalable diagnosis in settings with limited annotation resources.

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.

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

TL;DR

The study tackles the accuracy and efficiency of automated facial landmark labeling in medical imaging by introducing an iterative refinement strategy that leverages predictions from prior iterations, a confidence threshold of , and NMS filtering at a threshold of . The approach progressively expands labeled data across multiple facial landmark datasets (e.g., 300W, AFW, HELEN, IBUG, IFPW) and demonstrates measurable changes in labeling counts and performance metrics. Empirical evaluation using the YOLOv8n-pose framework across varied clinical contexts shows how precision, recall, AP, and MSE evolve through iterations, underscoring the method's potential to accelerate model training while maintaining label quality. The work argues for broader applicability to other medical landmarking tasks and highlights practical benefits for rapid, scalable diagnosis in settings with limited annotation resources.

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.
Paper Structure (14 sections, 3 figures, 2 tables)

This paper contains 14 sections, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Medical Imaging Analysis
  4. Facial Landmark Detection and Diagnosis
  5. Automated Data Labeling Techniques
  6. Method
  7. Automated Data Labeling
  8. Non-maximum Suppression Filtering
  9. Iterative Refinement Strategy
  10. Experiments
  11. Experimental Setup
  12. Implementation Details
  13. Comparison with baseline
  14. Conclusion

Figures (3)

  • Figure 1: The appearance of eyelid problems is provided in farber2020evaluation, and the green dots indicate the 51 keypoints used for annotating facial landmarks. (a) Patient with acquired left ptosis after left upper lid hematoma treated conservatively for a year. (b) The patient had a left tarsoaponeurectomy and was slightly overcorrected, which can easily be treated with downward lid massage.
  • Figure 2: The horizontal axis illustrates the provided labeling information and the generated labels for each iteration, and the vertical axis denotes the images in respective datasets, namely 300W, AFW, HELEN, IBUG, and IFPW sagonas2013300sagonas2013semisagonas2016300Zhu2012FaceDPLe2012InteractiveFF.
  • Figure 3: Total labels increase from 4437 to 6240 (40.6% rise), with training and validation labels rising from 3530 to 4950 (40.2% increase) and from 907 to 1290 (42.4% increase), respectively.