Illuminated Decision Trees with Lucid
David Mott, Richard Tomsett

TL;DR
This paper introduces the Illuminated Decision Tree method, combining neural network feature extraction with decision trees and Lucid visualizations to interpret models trained on microscope images of white blood cells, aiding development and explanation.
Contribution
It presents a novel approach that integrates feature visualization with decision trees for interpreting neural networks on challenging, less visually rich datasets.
Findings
Effective visualization of decision nodes in complex tasks
Improved model debugging and understanding
Enhanced explanation of model outputs to non-experts
Abstract
The Lucid methods described by Olah et al. (2018) provide a way to inspect the inner workings of neural networks trained on image classification tasks using feature visualization. Such methods have generally been applied to networks trained on visually rich, large-scale image datasets like ImageNet, which enables them to produce enticing feature visualizations. To investigate these methods further, we applied them to classifiers trained to perform the much simpler (in terms of dataset size and visual richness), yet challenging task of distinguishing between different kinds of white blood cell from microscope images. Such a task makes generating useful feature visualizations difficult, as the discriminative features are inherently hard to identify and interpret. We address this by presenting the "Illuminated Decision Tree" approach, in which we use a neural network trained on the task as…
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Taxonomy
TopicsCell Image Analysis Techniques · Explainable Artificial Intelligence (XAI) · Machine Learning and Data Classification
