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Leveraging AI Predicted and Expert Revised Annotations in Interactive Segmentation: Continual Tuning or Full Training?


Core Concepts
The author argues that Continual Tuning, through network design and data reuse, efficiently leverages AI predicted and expert revised annotations to enhance interactive segmentation tasks in the medical domain.
Abstract
Interactive segmentation combines AI algorithms with human expertise to improve dataset curation. Continual Tuning addresses issues like catastrophic forgetting and computational inefficiency by freezing shared networks for previous classes and reusing data. The method achieves faster training speeds without compromising performance, demonstrating potential for continual model improvement.
Stats
Continual Tuning achieves a speed 16× greater than training from scratch. The final average DSC scores can achieve about 76.1% and 78.8% for different backbones. Using Hybrid Data Continual Tuning improves the mean DSC score of the aorta by 10% compared to Full Training. The models trained on one dataset show better performance using all 200 CT scans.
Quotes
"Continual Tuning enables AI models to be fine-tuned efficiently (16× faster in our experiment) only with expert revised annotations." "Our experiments demonstrate that Continual Tuning achieves a speed 16× greater than repeatedly training AI from scratch."

Deeper Inquiries

How can human intervention impact the quality and consistency of annotations in interactive segmentation

Human intervention can significantly impact the quality and consistency of annotations in interactive segmentation. The subjectivity and variability introduced by human annotators during the revision process can lead to inconsistencies in annotations. These variations can affect the overall performance of AI models trained on these annotated datasets. Inaccurate or incomplete annotations may result in misleading classes, incorrect features being replayed, or even the creation of new erroneous classes. Therefore, ensuring a high level of expertise and standardization among annotators is crucial to maintaining annotation quality and consistency.

What are the limitations of using class-specific networks to prevent catastrophic forgetting

While using class-specific networks to prevent catastrophic forgetting can be effective in mitigating this issue, there are limitations to this approach. One key limitation is that pre-defined class-specific networks may not adapt well to evolving datasets with new classes introduced over time. As datasets grow or change, these fixed class-specific networks may become less effective at capturing the nuances of newly added classes. Additionally, managing a large number of class-specific networks for numerous categories could increase computational complexity and model maintenance overhead.

How might differences in dataset utilization affect model performance in interactive segmentation

Differences in dataset utilization can have a significant impact on model performance in interactive segmentation tasks. For instance, when training models across multiple datasets with varying annotation principles or levels of completeness, discrepancies in how different organs are annotated may arise. This inconsistency could lead to challenges when fine-tuning AI models with revised annotations from diverse sources as each dataset might follow distinct guidelines for annotation structures. Moreover, utilizing data from multiple datasets introduces complexities related to harmonizing annotations across different sources which could affect model generalization and performance negatively if not managed effectively.
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