Interpretable surrogate models to approximate the predictions of convolutional neural networks in glaucoma diagnosis
Date
2023Abstract
Deep learning systems, especially in critical fields like medicine, suffer from a significant drawback,
their black box nature, which lacks mechanisms for explaining or interpreting their decisions. In
this regard, our research aims to evaluate the use of surrogate models for interpreting
convolutional neural network (CNN) decisions in glaucoma diagnosis. Our approach is novel in
that we approximate the original model with an interpretable one and also change the input
features, replacing pixels with tabular geometric features of the optic disc, cup, and neuroretinal
rim. We trained CNNs with two types of images: original images of the optic nerve head and
simplified images showing only the disc and cup contours on a uniform background. Decision
trees were used as surrogate models due to their simplicity and visualization properties, while
saliency maps were calculated for some images for comparison. The experiments carried out with
1271 images of healthy subjects and 721 images of glaucomatous eyes demonstrate that decision
trees can closely approximate the predictions of neural networks trained on simplified contour
images, with R-squared values near 0.9 for VGG19, Resnet50, InceptionV3 and Xception
architectures. Saliency maps proved difficult to interpret and showed inconsistent results across
architectures, in contrast to the decision trees. Additionally, some decision trees trained as
surrogate models outperformed a decision tree trained on the actual outcomes without
surrogation. Decision trees may be a more interpretable alternative to saliency methods. Moreover,
the fact that we matched the performance of a decision tree without surrogation to that obtained
by decision trees using knowledge distillation from neural networks is a great advantage since
decision trees are inherently interpretable. Therefore, based on our findings, we think this
approach would be the most recommendable choice for specialists as a diagnostic tool.