Medical Image Annotation via Structured Key-Value Pairs
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Solution Overview
Problem
Current string-based annotation methods for medical images suffer from poor machine-interpretability and are limited to keyboard input, making them inefficient for annotating medical images effectively.
Innovation Solution
A system and method that utilize a probabilistic recommendation algorithm to select structured finding objects from a graph data structure, allowing users to annotate medical images using key-value pairs, which are more interpretable by machines and can be input through various interfaces like graphical user interfaces or speech recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If string-based annotation methods are used for medical images, then keyboard input is simple and direct, but machine-interpretability is poor and input flexibility is limited
Solution Approach 1:
The patent transforms the annotation format from unstructured strings to structured key-value pairs with predefined schemas. This parameter change enables both improved machine interpretability through structured data and input flexibility through multiple interface types (keyboard, speech, graphical selection), resolving the contradiction between these two requirements.
2Loss of information
If structured finding objects with key-value pairs are used, then machine-interpretability is improved, but user interaction complexity increases
Solution Approach 1:
The patent implements auto-completion functionality that automatically suggests and fills key-value pairs based on previously entered data and predefined schemas. This self-service mechanism reduces the manual effort required to create structured annotations while maintaining high machine interpretability, thus resolving the contradiction.
Solution Approach 2:
The system provides real-time feedback to users during annotation by suggesting relevant key-value pairs and validating input against predefined schemas. This feedback mechanism guides users through the structured annotation process, making it easier to operate while preserving machine interpretability.
3Measurement precision
If probabilistic recommendation algorithm is implemented, then annotation accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent implements a probabilistic recommendation algorithm that provides annotation suggestions rather than requiring complete manual entry or complex automated processing. This partial action approach improves annotation accuracy through intelligent recommendations while keeping computational complexity manageable by not requiring full automated annotation.
4Productivity
If auto-completion functionality is added, then productivity is improved, but system complexity increases
Solution Approach 1:
The patent pre-defines annotation schemas, key-value pair structures, and recommendation rules before the annotation process begins. This preliminary action enables auto-completion functionality that improves productivity while keeping system complexity manageable by using predefined templates rather than requiring complex real-time generation of annotation structures.
Data Source
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AI summary
A system and method are provided for enabling a user to annotate a medical image. A collection of key- value pairs is provided, in which a key represents an image- observable quantity and a value represents the value of the image-observable quantity. A collection of structured finding objects is provided, wherein each structured finding object represents a set of key- value pairs, each set of key- value pairs representing a different annotation of the medical image. The user is enabled to select one or more of the collection of key-value pairs, thereby obtaining a user-selected structured finding object which represents a preliminary annotation of the medical image by the user. At least one recommended structured finding object is selected by using the user-selected structured finding object as input to a probabilistic recommendation algorithm. Feedback is provided to the user on the basis of the recommended structured finding object. The annotation is well suited for, e.g., pointer-based selection via a graphical user interface, speech recognition, etc. Moreover, machine interpretability may be improved compared to conventional string-based annotation.