Input-Dependent Fisher Information for Local Sensitivity Analysis of Medical Image Classifiers
arXiv:2606.16362v1 Announce Type: cross Abstract: Deep neural networks have achieved strong performance in medical image classification, but often work like black-box. Commonly used post-hoc interpretation methods often provide heuristic visualizations whose relationship to the classifier's predictive distribution is indirect. This work introduces a local sensitivity analysis framework based on the input-dependent Fisher Information Matrix (iFIM) of a trained classifier. The iFIM characterizes how the classifier's predictive distribution changes under infinitesimal perturbations of the input i
The increasing deployment of AI in sensitive applications like medicine necessitates robust interpretability and transparency methods to build trust and ensure reliability.
This development offers a more scientifically grounded approach to understanding and validating AI decisions in critical fields, moving beyond heuristic visualizations.
The ability to perform local sensitivity analysis with input-dependent Fisher Information Matrix provides a more rigorous method for evaluating model robustness and identifying failure modes.
- · Medical AI developers
- · Regulatory bodies
- · Patients
- · Responsible AI researchers
- · Black-box AI models
- · Developers solely relying on heuristic interpretation methods
Improved trust and adoption of AI in high-stakes environments like healthcare due to better interpretability.
New regulatory standards and compliance frameworks for AI explainability may emerge, requiring such rigorous analysis.
Increased public confidence could accelerate AI integration into other sensitive sectors, driven by demand for transparent and auditable systems.
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Read at arXiv cs.AI