Audit Network for ML Robustness in Clinical Workflows
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Solution Overview
Problem
Machine learning models in clinical workflows face challenges in robustness when encountering input data that is out-of-distribution or contains artifacts, leading to inaccurate medical analysis tasks.
Innovation Solution
A machine learning-based audit network is implemented to determine the robustness of medical analysis networks by evaluating input medical data and task results, using normalizing flows models to assess the data's similarity to the training dataset and flagging non-robust predictions, with options to retrain the network or provide alternate results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a machine learning model is trained on a large training dataset to cover a wide range of variations, then the model's general performance is improved, but the model still fails when encountering out-of-distribution data or artifacts in clinical workflows
Solution Approach 1:
The patent introduces an audit network as an intermediary component between the medical analysis network and the final output. This audit network specifically evaluates whether input data is out-of-distribution or contains artifacts, and determines the robustness of predictions. The audit network acts as a mediator that catches failures of the main model without requiring retraining, thus resolving the contradiction between general performance and adaptability to unseen data distributions.
2Ease of operation
If the machine learning model is trained offline and deployed at a clinical site, then the model can be integrated into clinical workflows for decision making, but the model lacks the ability to adapt to new data distributions encountered in practice
Solution Approach 1:
The patent implements robustness evaluation as a preliminary action before the medical analysis network makes final predictions. The audit network pre-assesses whether input data is out-of-distribution or contains artifacts before the main model processes it. This preliminary check allows the system to handle new data distributions by flagging problematic inputs without requiring online retraining or complex adaptation mechanisms, thus maintaining ease of deployment while improving adaptability.
3Adaptability or versatility
If the model is asked to perform predictions on datasets significantly different from training data, then the model may be applied to new clinical scenarios, but the predictions become inaccurate due to out-of-distribution inputs
Solution Approach 1:
The patent implements a feedback mechanism where the audit network continuously monitors the robustness of predictions made by the medical analysis network. When the audit network detects out-of-distribution data or artifacts, it provides feedback that flags the prediction as unreliable. This feedback loop allows the system to maintain adaptability to new clinical scenarios while preserving measurement precision by identifying and flagging inaccurate predictions without requiring model retraining.
Data Source
AI summary
Systems and methods for determining a robustness of a machine learning based medical analysis network for performing a medical analysis task on input medical data are provided. Input medical data is received. Results of a medical analysis task performed based on the input medical data using a machine learning based medical analysis network are received. A robustness of the machine learning based medical analysis network for performing the medical analysis task is determined based on the input medical data and the results of the medical analysis task using a machine learning based audit network. The determination of the robustness of the machine learning based medical analysis network is output.