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Enabling Autonomous Data Annotation in Mammography Image: A Human-in-the-Loop Reinforcement Learning Approach.
Leonardo C da Cruz1, Cesar A Sierra-Franco2, Alberto Raposo2
1Tecgraf Institute and Department of Informatics, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil. leonardocardia@gmail.com.
Journal of Imaging Informatics in Medicine
|January 6, 2026
Summary
This study introduces a novel Deep Reinforcement Learning (DRL) approach, "Try a Little More" (TLM), to automate annotation generation for object detection. TLM significantly reduces human effort and improves training data quality for AI models.
Area of Science:
- Computer Vision
- Machine Learning
- Artificial Intelligence
Background:
- Supervised learning models in computer vision require large labeled datasets, which are expensive and time-consuming to create.
- Automating the annotation process is crucial for efficient training data preparation.
Purpose of the Study:
- To present a Deep Reinforcement Learning (DRL) based approach for automatic annotation generation, reducing human effort in supervised learning.
- To introduce the "Try a Little More" (TLM) methodology, inspired by constructivist teaching, to enhance agent learning through human guidance and active learning.
Main Methods:
- Developed a virtual agent trained with human guidance using constructivist teaching principles.
- Implemented active learning within the TLM approach to identify uncertain cases and request human intervention.
- Evaluated the agent's ability to autonomously create bounding box annotations on a mammography dataset.
Main Results:
- The TLM approach generated 414 new annotations with high IoU (0.86) and F1-score (0.92).
- Utilizing TLM annotations significantly improved a YOLO detector's mAP from 0.52 to 0.91 (75% improvement).
- Achieved a 35% reduction in human intervention for annotation generation.
Conclusions:
- The proposed TLM methodology effectively accelerates annotation creation and enhances training data quality.
- This approach advances Data-Centric AI by combining human advice with reinforcement learning for efficient data annotation, especially in data-scarce domains.
