  A novel evolutionary approach for Explainable Artificial Intelligence is
presented: the "Evolved Explanations" model (EvEx). This methodology consists
in combining Local Interpretable Model Agnostic Explanations (LIME) with
Multi-Objective Genetic Algorithms to allow for automated segmentation
parameter tuning in image classification tasks. In this case, the dataset
studied is Patch-Camelyon, comprised of patches from pathology whole slide
images. A publicly available Convolutional Neural Network (CNN) was trained on
this dataset to provide a binary classification for presence/absence of lymph
node metastatic tissue. In turn, the classifications are explained by means of
evolving segmentations, seeking to optimize three evaluation goals
simultaneously. The final explanation is computed as the mean of all
explanations generated by Pareto front individuals, evolved by the developed
genetic algorithm. To enhance reproducibility and traceability of the
explanations, each of them was generated from several different seeds, randomly
chosen. The observed results show remarkable agreement between different seeds.
Despite the stochastic nature of LIME explanations, regions of high explanation
weights proved to have good agreement in the heat maps, as computed by
pixel-wise relative standard deviations. The found heat maps coincide with
expert medical segmentations, which demonstrates that this methodology can find
high quality explanations (according to the evaluation metrics), with the novel
advantage of automated parameter fine tuning. These results give additional
insight into the inner workings of neural network black box decision making for
medical data.
