Table 4 Summary of the notations.

From: Improving explainable AI with patch perturbation-based evaluation pipeline: a COVID-19 X-ray image analysis case study

Notations

Description

\({\textbf{x}}\), \({\textbf{x}}_i\)

Original input images

\(\tilde{{\textbf{x}}}\), \(\tilde{{\textbf{x}}}_i\)

Patch-perturbed images

\(\hat{{\textbf{x}}}\), \(\hat{{\textbf{x}}}_i\)

Recovered images

y, \(y_i\)

Original labels

\({\tilde{y}}\), \({\tilde{y}}_i\)

Target labels for patched images

\(f(\cdot )\)

Baseline classification model

\(f^\prime (\cdot )\)

Poisoned classification model

\({\textbf{p}}\), \({\textbf{p}}_s\), \({\textbf{p}}_d\)

Attached patches for perturbation

\({\textbf{m}}\)

Binary masks indicating the patch locations

\({\textbf{s}}\), \({\textbf{s}}_i\)

Saliency maps

M

Number of cleaned samples in test set

N

Number of poisoned samples in test set

\(\varepsilon\)

Pixel-wise perturbation amount

\(\alpha\)

Poisoning ratio