Natural Language Descriptors for ML Failure Mode Analysis
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Solution Overview
Problem
Machine learning models in medical imaging, constrained by training data and architecture, often fail to generate accurate outputs, and existing methods for identifying failure modes are time-consuming and imprecise.
Innovation Solution
A multi-modal representation learning framework generates natural language descriptors for failure cases in machine learning models, allowing for the identification of factors causing or associated with failure modes through cluster-based analysis and histogram analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual methods are used to identify failure modes, then thorough analysis can be achieved, but the process is time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual mechanical analysis with automated computer-based processing. The system automatically generates failure mode descriptors by processing model predictions, test data, and failure case characteristics through computational algorithms, eliminating the need for manual time-consuming analysis while maintaining or improving identification accuracy.
Solution Approach 2:
The system enables self-service by automatically generating failure mode descriptors without requiring extensive manual intervention. The computer processor autonomously processes test data, compares predictions against actual outcomes, and generates comprehensive failure mode descriptions, allowing the system to identify and characterize its own failures independently.
2Loss of information
If comprehensive failure analysis is performed manually, then detailed understanding of failure causes can be achieved, but the complexity and difficulty of detection increase
Solution Approach 1:
The patent segments the failure analysis process into distinct automated components: data collection, prediction comparison, descriptor generation, and pattern identification. Each component handles a specific aspect of failure analysis, breaking down the complex task into manageable automated steps that reduce detection difficulty while preserving information completeness.
Solution Approach 2:
The system introduces an intermediary automated processing layer between the machine learning model and the failure analysis. This intermediary computer-based system generates structured failure mode descriptors that bridge the gap between raw model outputs and human-understandable failure causes, simplifying detection while maintaining information integrity.
3Productivity
If machine learning models are deployed without comprehensive failure mode identification, then productivity increases, but reliability decreases due to undetected failure modes
Solution Approach 1:
The patent implements preliminary automated failure mode identification before model deployment. The system pre-generates failure mode descriptors and identifies potential failure causes during the development and testing phase, allowing teams to address reliability issues before deployment while maintaining rapid deployment capabilities through automation.
Solution Approach 2:
The system incorporates feedback mechanisms where failure mode identification results are fed back into the model development process. The automated analysis of test failures provides feedback that guides model retraining and improvement, enhancing reliability while the automation maintains productivity by efficiently processing feedback loops.
Data Source
AI summary
In one embodiment, a method is provided for determining one or more failure modes of a machine learning model. In accordance with certain such embodiments, one or more images are accessed or acquired from a first source. The one or more images are processed using a machine learning model. The machine learning model outputs one or more processed images. One or more failure cases of the machine learning model are detected in the one or more processed images. One or more respective images corresponding to the one or more failure cases are processed using a text description generating framework configured to generate one or more text descriptors for each image corresponding to a failure case. One or more failure modes of the machine learning model are determined based on the text descriptors generated for the images corresponding to the failure cases.