Label Inference System for Automated Chest X-Ray Annotation
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Solution Overview
Problem
The development of automated chest X-ray interpretation systems faces challenges in achieving human-level accuracy due to the high cost and time required for creating large, high-quality labeled datasets, which are often biased and noisy, and require expertise from radiologists.
Innovation Solution
A label inference system that combines a data generator for creating labeled training and test sets, a data trainer for model training, a determiner for evaluating model performance, and a data inference unit for incremental human annotation and online updates, using a weighted average method to improve label quality and reduce annotation costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large-scale high-quality labeled datasets are obtained through expert annotation, then model accuracy is improved, but annotation cost and time consumption increase significantly
Solution Approach 1:
The system enables self-service through automated label inference where the model generates its own training labels by inferring from unlabeled data. The label inference unit automatically processes unlabeled chest X-ray images and generates labels without requiring continuous expert intervention, allowing the system to self-improve through iterative training and inference cycles.
Solution Approach 2:
The system performs preliminary action by pre-processing unlabeled data through the label inference unit before expert annotation is needed. The model generates preliminary labels that can be directly used for training, reducing the need for time-consuming expert annotation of every dataset.
2Measurement precision
If expert radiologists annotate large amounts of data to ensure label quality, then label accuracy is improved, but cost and time consumption increase
Solution Approach 1:
The system reduces dependency on expert radiologists by enabling the model to generate its own training labels through automated inference. The label inference unit processes unlabeled data and generates labels automatically, with experts only needing to review and correct low-confidence predictions rather than annotating all data from scratch.
Solution Approach 2:
The system applies partial action by having experts annotate only a subset of data - specifically, only the low-confidence samples identified by the label inference unit. This partial annotation approach maintains label quality for critical cases while significantly reducing overall annotation cost and effort.
3Reliability
If consensus-based labeling by multiple radiologists is used, then label reliability is improved, but time consumption and resource requirements increase
Solution Approach 1:
The system implements feedback mechanisms where the model's confidence scores guide the annotation process. Low-confidence predictions are fed back to experts for review and correction, while high-confidence predictions are automatically accepted. This feedback loop maintains label reliability by focusing expert attention on uncertain cases while improving overall annotation efficiency.
Solution Approach 2:
The system applies partial action by having multiple experts review only the low-confidence samples rather than requiring full consensus on all datasets. This selective review process maintains reliability for critical cases while significantly improving annotation efficiency by avoiding redundant reviews of high-confidence predictions.
Data Source
AI summary
Provided is a label inference system including a data generator configured to generate a training set and a test set, each including a plurality of images labeled with experts' annotations, a data trainer configured to perform training for a base model based on the generated training set and test set, a determiner configured to identify whether an evaluation metric f1 of the training model satisfies a base evaluation metric f1base, and a data inference unit configured to perform inference using the training set, the test set, and an unlabeled data set with the training model satisfying the base evaluation metric f1base.