Gesture-Based Surgical Content Annotation for Faster Labeling
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Solution Overview
Problem
Obtaining reliable annotations for training machine learning models in surgical applications is challenging due to the significant time investment required from experts, making it difficult to gather sufficient training data.
Innovation Solution
An annotation system that facilitates efficient annotation of surgical content through a user interface, allowing annotators to provide labels via gestures or selections, with predefined labels and rules, and updates the machine learning model based on collected labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional annotation methods are used with expert physicians, then annotation reliability is improved, but time investment and cost increase significantly
Solution Approach 1:
The patent introduces a semi-automated annotation system that acts as an intermediary between raw surgical videos and final training data. The system uses pre-processing algorithms to generate initial annotations, which are then reviewed and refined by annotators, reducing their time investment while maintaining reliability through multiple review stages and quality control mechanisms.
Solution Approach 2:
The system performs preliminary annotation actions automatically before human annotators review the content. By pre-processing videos and generating initial label proposals, the system prepares the data in advance, allowing annotators to focus only on verification and refinement rather than creating annotations from scratch, thus reducing time investment while preserving reliability.
2Quantity of substance
If more training data is collected, then model training quality is improved, but the time and resources required for annotation increase
Solution Approach 1:
The annotation system incorporates self-service mechanisms where the algorithm automatically learns from annotated data and improves its pre-processing capabilities over time. This self-improving loop allows the system to handle larger volumes of training data more efficiently, as each batch of annotated data makes the system faster and more accurate at generating initial annotations, thereby increasing overall annotation productivity.
Solution Approach 2:
The system enables continuous annotation workflows where multiple annotators can work simultaneously on different portions of the training dataset. The pipeline processes videos continuously through pre-processing, annotation, and quality control stages without interruption, maximizing the utilization of annotator time and enabling collection of large quantities of training data efficiently.
3Measurement precision
If expert annotators are used, then annotation accuracy is improved, but the complexity and cost of the annotation process increase
Solution Approach 1:
The annotation process is segmented into distinct stages: automatic pre-processing by algorithms, initial annotation generation, human review and refinement, and quality control verification. This segmentation allows different types of annotators with varying expertise levels to work on different stages, reducing the need for highly expert annotators throughout the entire process while maintaining high annotation accuracy through specialized tasks at each stage.
Data Source
AI summary
An annotation system facilitates collection of labels for images, video, or other content items relevant to training machine learning models associated with surgical applications or other medical applications. The annotation system enables an administrator to configure annotation jobs associated with training a machine learning model. The job configuration controls presentation of content items to various participating annotators via an annotation application and collection of the labels via a user interface of the annotation application. The annotation application enables the participating annotators to provide inputs in a simple and efficient manner, such as by providing gesture-based inputs or selecting graphical elements associated with different possible labels.