Saliency Mapping for Medical Image Classification Transparency
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Solution Overview
Problem
Current deep neural network introspection methods are inadequate for providing clear insights into image classification decisions, especially in medical diagnostic imaging, where images are complex and noisy, leading to mistrust in AI systems.
Innovation Solution
A saliency mapping method that involves submitting variations of video clip imagery to a neural network trained for organ feature detection, measuring changes in probability, and correlating these changes to specific pixels, with graphical indicators overlaid to highlight dispositive pixels based on threshold changes, and optionally including noise addition and filtering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural networks are used for image recognition, then classification accuracy is improved, but decision-making transparency deteriorates
Solution Approach 1:
The patent introduces an intermediary system that acts as a bridge between the black-box neural network and the user. This intermediary captures intermediate activation values from different layers of the network, processes them through gradient computation, and generates saliency maps that highlight important regions. This mediator translates the opaque internal representations into interpretable visual feedback without altering the original network's classification functionality.
Solution Approach 2:
The patent replaces direct observation of neural network internal states with a computational substitution approach. Instead of trying to directly interpret complex high-dimensional activation patterns, the system uses gradient-based computational mechanics to compute saliency values that substitute for direct transparency. This computational substitution transforms the unobservable internal decision processes into observable gradient-based importance measures.
2Loss of information
If neural network introspection is implemented, then decision visibility is improved, but interpretation accuracy deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where intermediate activation values from the neural network are fed back through a gradient computation process. The system computes gradients of the output probability with respect to input pixels, using this feedback to generate saliency maps. This feedback loop allows the system to iteratively refine its interpretation by comparing predicted saliency regions against actual activation patterns across multiple network layers.
Solution Approach 2:
The patent applies partial action by focusing computational resources on computing gradients only for the most relevant input regions rather than processing the entire image uniformly. The system computes saliency values selectively, concentrating computational effort on areas that contribute most to the classification decision, thereby improving interpretation accuracy without requiring exhaustive analysis of all pixels.
3Measurement precision
If pixel-wise saliency mapping is performed, then feature identification precision is improved, but computational complexity deteriorates
Solution Approach 1:
The patent segments the computational process into distinct stages: forward propagation to obtain activation values, backward propagation to compute gradients, and saliency map generation. By dividing the complex pixel-wise analysis into these segmented phases, the system can efficiently compute gradients only when needed and reuse intermediate results across multiple pixels, reducing overall computational complexity while maintaining precision.
Solution Approach 2:
The patent performs preliminary computation of activation values and gradients during the forward and backward propagation phases before generating the final saliency map. By pre-computing these intermediate values that are reused in saliency calculations, the system avoids redundant computations and reduces the overall computational burden of pixel-wise feature identification.
Data Source
AI summary
A saliency mapping method includes displaying video clip imagery of an organ in a display of a computer and submitting the imagery to a neural network trained to produce a probability of an existence of a physical feature of the organ. In response, the probability is received along with a pixel-wise mapping of dispositive pixels in the imagery resulting in the probability. Variations of the imagery are then repeatedly resubmitted to the neural network, each including a change to one or more of the pixels in the imagery. Thereafter, for each resubmission, a change is measured in the probability and then correlated to the changed pixels. Finally, a graphical indicator is overlain on the display of the imagery corresponding to each of the pixels determined through the repeated resubmission to be dispositive based upon a threshold measured change in probability.