Probability Distribution Visualization for Neural Network Interpretability
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Solution Overview
Problem
Existing techniques for cell classification using artificial neural networks struggle to provide interpretable classification performance, as the meaning of classification conditions within the networks is not readily understandable by humans, making it difficult to evaluate the performance effectively.
Innovation Solution
A display control device and method that obtain and display the probability distribution of classification results using a graph with a probability axis, allowing humans to interpret the classification performance of artificial neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If artificial neural networks are used for cell classification, then classification performance is improved, but interpretability of classification conditions deteriorates
Solution Approach 1:
The patent introduces a probability distribution visualization as an intermediary between the neural network's internal classification conditions and human understanding. By displaying the probability distribution of classification results, the system mediates the information loss, allowing humans to interpret the neural network's decision-making process without exposing the complex internal weights and conditions.
2Ease of operation
If classification conditions of artificial neural networks are made more interpretable, then ease of operation is improved, but classification performance may deteriorate
Solution Approach 1:
The patent extracts the probability distribution information from the neural network's internal processing and presents it separately as a visualization. This extraction allows users to evaluate classification performance without needing to understand the complex internal structure of the neural network, thereby improving ease of operation while maintaining the network's complexity for optimal performance.
Data Source
AI summary
A display method, system, device, and related computer programs can present the classification performance of an artificial neural network in a form interpretable by humans. In these methods, systems, devices, and programs, a probability calculator (i.e., a classifier) based on an artificial neural network calculates a classification result for an input image in the form of a probability. The distribution of classification-result probabilities are displayed using at least one display axis of a graph as a probability axis.