Medical Image Recognition Model for Reducing Annotation Time
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Solution Overview
Problem
Current medical image recognition methods are hindered by high manual annotation costs and errors, leading to reduced accuracy and reliability, especially with the large volume of medical images generated by technologies like CT, MRI, and US.
Innovation Solution
A medical image recognition method that involves preprocessing medical images to extract relevant areas, using a trained model to determine recognition results, and integrating this process into a computer device for efficient and accurate image classification, reducing manual annotation time and errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used for medical images, then annotation can be performed with human judgment, but annotation costs and time consumption increase significantly
Solution Approach 1:
The system performs preliminary automated annotation using pre-trained recognition models before final manual confirmation. This preliminary action filters out obvious cases and prepares draft annotations, reducing the time required for manual processing while maintaining accuracy through subsequent human review of only the critical cases.
Solution Approach 2:
An automated recognition model serves as an intermediary between the medical image and the final annotation result. The model generates preliminary annotations that are then refined by human annotators, acting as a mediator that handles routine cases while allowing human judgment for complex or uncertain cases.
2Ease of operation
If manual annotation is used for medical images, then annotation can be performed flexibly, but annotation errors increase and reliability decreases
Solution Approach 1:
The system implements a feedback mechanism where automated model predictions are reviewed and corrected by human annotators, and these corrections are used to retrain and improve the model. This continuous feedback loop reduces annotation errors over time while maintaining the flexibility of manual review for edge cases.
Solution Approach 2:
The automated recognition model acts as an intermediary that provides consistent, error-free annotations for standard cases, while human annotators serve as a secondary intermediary for reviewing and correcting the model's output, thereby reducing overall annotation errors while preserving operational flexibility.
3Productivity
If automated recognition models are used, then annotation efficiency increases, but model training data requirements and system complexity increase
Solution Approach 1:
The system segments the annotation process into two parts: automated processing for standard cases and manual review for complex cases. This segmentation allows the automated model to handle the majority of cases efficiently while reducing the complexity burden on any single component, as the manual review step handles only the remaining challenging cases.
Solution Approach 2:
The recognition model performs self-service by automatically generating annotations without human intervention for clear-cut cases. This self-service capability increases productivity for routine annotations while reducing system complexity by eliminating the need for manual processing of standard cases, reserving human resources only for complex scenarios.
Data Source
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AI summary
A medical image identifying method, comprising: obtaining a set of medical images to be identified, the set of medical images to be identified comprising at least one medical image to be identified (101); extracting an area to be identified corresponding to each one medical image in the set of medical images, the area belonging to a part of the medical image (102); determining an identification result of each area by means of a medical image identification model, the medical image identification model being obtained by training according to a medical image sample set, the medical image sample set comprising at least one medical image sample, each medical image sample carrying corresponding label information, the label information being used for indicating the type of the medical image sample, and the identification result being used for indicating the type of the medical image (103). Also disclosed are a model training method and a server. The manual labeling costs and time costs are greatly saved, and higher reliability and accuracy are achieved..