Explainable Deep Learning Methods in Medical Diagnosis: A Survey • 7
understand the contribution of dierent features in the model prediction, or by analytically determining the
contribution of dierent features to the model prediction. On the other hand, intrinsic models, also known as
in-model approaches or inherently interpretable models are self-explainable since they are designed to produce
human-understandable representations from the internal model features.
3.2 Classical XAI Methods
The rst attempts to explain deep learning models relied on the post-hoc analysis of the models. In spite of the
criticism that post-hoc approaches have been recently subjected [
105
], they are still being used in many domains
of medical imaging, and their understanding is important to explain the advances on the topic of interpretable
deep learning. As such, the following sections briey describe the most popular XAI algorithms according to the
two major categories of post-hoc analysis.
3.2.1 Perturbation-based methods. The rationale behind perturbation-based methods is to perceive how a
perturbation in the input aects the model’s prediction. Two of the most representative perturbation-based
methods are LIME [101] and SHAP [77]; and an occlusion-based method [151].
LIME
. LIME [
101
] stands for Local Interpretable Model-agnostic Explanations. As the name suggests, it can
explain any black-box model, and according to the XAI taxonomy is a post-hoc, model-agnostic method providing
local explanations. The intuition behind LIME is to approximate the complex model (black-box model) locally
with an interpretable model, usually denoted as local surrogate model. Thus, an individual instance is explained
locally using a simple interpretable model around the prediction, such as linear models or decision trees. Figure 2
a) provides an intuitive illustration of the overall functioning of LIME.
In order to approximate the model prediction locally, a new dataset consisting of perturbed samples conditioned
on their proximity to the instance being explained is used to t the interpretable model. The labels for those
perturbed samples are obtained through the complex model. In the case of tabular data, the perturbed instances
are sampled around the instance being explained, by randomly changing the feature values in order to obtain
samples both in the vicinity and far away from the instance being explained. Analogously, when LIME is applied
to the image classication problem, the image being explained is rst segmented into superpixels, which are
groups of pixels in the image sharing common characteristics, such as colour and intensity. Then, the perturbed
versions of the original data are obtained by randomly masking out a subset of superpixels, resulting in an
image with occluded patches. The new dataset used to t the interpretable model consists of perturbed versions
of the image being explained, and the superpixels with the highest positive coecients in the interpretable
model suggest they largely contributed to the prediction. Thus, they will be selected as part of the interpretable
representation that is simple a binary vector indicating the presence or absence of those superpixels.
SHAP
. SHAP [
77
] was inspired on the Shapley values from the cooperative game theory [
113
] and operates
by determining the average contribution of a feature value to the model prediction using all combinations of the
features powerset. As an example, given the task of predicting the risk of stroke based on age, gender and Body
Mass Index (BMI), the SHAP explanations for a particular prediction are given in terms of the contribution of
each feature. This contribution is determined from the change observed in model prediction when using the 2
𝑛
combinations from the features powerset, where the missing features are replaced by random values. Figure 2 b)
illustrates the above-described example. Similar to LIME, SHAP is a local model-agnostic interpretation method
that can be applied to both tabular and image data. In the case of tabular data, the explanation is given in the
form of importance values to each feature. In the case of image data, it follows a similar procedure to the LIME,
by calculating the Shapley values for all possible combinations between superpixels. Several variations of SHAP
method were proposed to approximate Shapley values in a more ecient way, namely KernelSHAP, DeepSHAP
and TreeSHAP [76].
, Vol. 1, No. 1, Article . Publication date: May 2022.
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