Prototype-Based Image Classification With Visual Reasoning
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Solution Overview
Problem
Deep learning models for glaucoma detection lack transparency and interpretability, leading to mistrust in their decision-making processes due to their black-box nature, which is a critical issue in high-stakes medical diagnostics.
Innovation Solution
A prototype-based interpretable model is developed using a conditional generative model to generate prototype images associated with class labels, allowing for similarity scores between input images and prototypes to be calculated, thereby providing transparent and interpretable classification results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models are used for glaucoma detection, then diagnostic accuracy is improved, but interpretability deteriorates due to black-box nature
Solution Approach 1:
The patent creates synthetic prototype images that copy and represent the essential features of real medical images. These prototypes serve as interpretable representations that maintain the diagnostic information while being visually understandable, thus resolving the contradiction between accuracy and interpretability by providing a copy-based explanation mechanism
Solution Approach 2:
The patent introduces prototypes as an intermediary between the black-box deep learning model and the final interpretation. These prototypes act as a mediator that translates the model's internal representations into visually understandable forms, allowing users to understand the decision-making process without sacrificing diagnostic accuracy
2Reliability
If black-box models are used for medical diagnostics, then diagnostic performance is improved, but trust in model decisions deteriorates
Solution Approach 1:
The patent uses visual transformations and enhancements in prototype generation to make the model's reasoning visible. By applying visual changes and optimizations to the prototype images, the system makes the abstract decision criteria tangible and interpretable, thereby building trust while maintaining diagnostic performance
Solution Approach 2:
The system creates visual copies (prototypes) that represent the model's decision rationale. These synthetic images serve as transparent explanations of why the model made certain predictions, allowing medical professionals to verify and trust the diagnostic performance without relying on opaque black-box reasoning
3Loss of information
If prototype images are generated for each prototype vector, then interpretability is improved, but computational complexity increases
Solution Approach 1:
The patent generates prototypes selectively and efficiently, creating only the necessary number of prototype images needed for interpretation rather than exhaustive generation. This partial action approach provides sufficient interpretability while avoiding excessive computational overhead that would arise from generating all possible prototypes
Solution Approach 2:
The system pre-computes and stores prototype vectors during the training phase, so that during inference only the generation and comparison with pre-existing prototypes is needed. This preliminary action shifts computational burden to the training phase, reducing the complexity during actual diagnostic operations
Data Source
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AI summary
Interpretation of images has a variety of applications. For instance, medical image diagnostics such as glaucoma remains one of the leading causes of irreversible blindness, its timely detection being imperative to avoiding permanent visual impairment. Conventionally, the sole focus on increasing the accuracy of predictions has resulted in a lack of trust due to the black box nature of such models. Present disclosure provides systems and methods that implement a conditional generative model along with a classifier that enable learning of class-specific prototypes, which capture the general characteristics or concepts of the pathology, and then use the actual visualized prototypes in the decision-making process by computing the similarity between them and the query image, as a result revealing the underlying model's reasoning process.