Generative Visual Explanation Masks for Interpretable AI Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing AI systems in medical imaging lack transparency, particularly in classification tasks, making it difficult for human operators to trust their decisions due to the lack of explainable AI methods that generate noisy and difficult-to-interpret saliency maps.
Innovation Solution
A framework that trains a generative model to generate new images resembling input images but classified as alternate classes, using a conditional generative adversarial network (cGAN) to produce high-quality explanation masks that reduce noise and enhance interpretability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If saliency-based methods are used to provide transparency, then the classifier output becomes differentiable relative to input, but noisy patterns are generated that are difficult to interpret
Solution Approach 1:
The method performs preliminary actions by generating multiple perturbed versions of the input image before computing the final saliency map. By creating several variations with different noise realizations and computing their average, the method prepares the data in advance to cancel out noise while preserving the true saliency signal, thus improving interpretability before the final measurement is made.
Solution Approach 2:
The method applies different quality characteristics to different parts of the computation process. By generating multiple local perturbations of the input image and averaging their gradients, the method preserves local saliency information while eliminating local noise patterns. This local quality differentiation allows the final saliency map to maintain high interpretability while preserving transparency.
2Reliability
If more training samples are used to improve classification accuracy, then the manifold projection becomes more accurate, but the complexity of the system increases
Solution Approach 1:
The method introduces an intermediary computational process between the input image and the saliency map. By using gradient-based backpropagation through the neural network as an intermediary, the method can compute saliency without requiring additional training samples or increasing system complexity. The intermediary gradient computation allows accurate saliency measurement while maintaining the existing training dataset size.
Solution Approach 2:
The method creates multiple copies of the input image with different perturbations rather than requiring multiple training samples. By generating several perturbed versions of the same input image and computing their average gradient, the method achieves noise reduction without needing to expand the training dataset or increase system complexity. This copying approach provides the necessary statistical robustness through computational replication rather than data replication.
3Difficulty of detecting and measuring
If attention mechanisms are added to localize relevant regions, then the system can point to relevant locations, but the system does not provide actual explanations for classification
Solution Approach 1:
The method inverts the traditional approach by not trying to directly identify important regions, but rather by identifying which regions, when perturbed, cause the largest change in classification output. By computing gradients of the output with respect to the input, the method inverts the problem from detection to explanation, providing both region localization and actual explanation of why those regions matter for the classification decision.
Data Source
AI summary
A framework for visual explanation of classification. The framework trains (204) a generative model to generate new images that resemble input images but are classified by the classifier as belonging to one or more alternate classes. At least one explanation mask may then be generated (206) by performing optimization based on a current input image and a new image generated by the trained generative model from the current input image.