## 3 XAI Approaches for Chest X-ray Image Analysis and Report Generation
### Attention-Based Explanation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Explainable AI Techniques** & **Studies** & **Year** & **Chest X-ray Analysis** \\ \hline Class Activation Mapping (CAM) & & & \\ and its variations. & & & \\ CAM creates a heat map reflecting & & 2021 & COVID-19 \\ the importance of the feature maps. & & 2020 & Pneumothorax identification \\  & & 2020 & COVID-19 \\  & & 2020 & COVID-19 \\  & & 2020 & COVID-19 \\  & & 2020 & COVID-19 \\  & & 2019 & Classification of Chest pathologists \\  & & 2019 & Labelling of Chest X-ray pathologies \\  & & 2018 & Detection of Pathology diseases \\  & & 2018 & Thoracic disease classification \\  & & 2018 & Detection of Lung Cancer \\ \hline Attention-based Explanation. & & & \\ It focuses on the most relevant & & 2021 & COVID-19 Detection \\ features of the input and uses them & & 2020 & Multiple pathologies \\ to explain predictions. & & 2019 & Thoracic disease classification \\  & & 2019 & Classification of Thoracic Diseases \\  & & 2019 & Pulmonary lesions \\  & & 2019 & Diagnose Chest Pathology \\  & & 2018 & Emphysema Detection \\  & & 2017 & Enlarged Heart \\ \hline Local Interpretable Model- & & & \\ Agnostic Explanations (LIME). & & 2021 & COVID-19 \\ LIME simplifies prediction models & & 2021 & COVID-19 \\ to create explanations that are & & 2021 & COVID-19 \\ simpler to understand. & & 2021 & COVID-19 \\  & & 2021 & COVID-19 \\  & & 2021 & COVID-19 \\  & & 2021 & Pneumonia detection \\  & & 2020 & COVID-19 including multiple pathologies \\ \hline Layer-wise Relevance Propagation (LRP). & & & \\ LRP propagates the predictions & & 2022 & COVID-19 \\ backwards through the layers of the & & 2021 & Pneumonia detection \\ model to compute the importance & & 2021 & COVID-19 \\ of each part of the input. & & 2021 & COVID-19, lung segmentation \\  & & 2020 & COVID-19 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of explainable AI methods for chest X-ray image analysis. The last column explains the disease or anomaly predicted by a model, and the first column indicates the XAI method used to explain the decisions of the model. A recurrent attention model is proposed in [53] that uses reinforcement learning to focus on the parts of an X-ray image that are likely to display pulmonary lesions. Spatial attention maps were used to predict the regions where a pathology might exist. This improves the accuracy of classification. A stochastic attention-based approach is presented in [56] to predict which areas in chest X-rays should be visually explored to look for a specific radiological anomaly: enlarged heart. Thorax diseases typically occur in localised disease-specific regions. Irrelevant noisy regions in chest X-ray images are those regions which either do not portray any information about the disorder or do not present a clear depiction of an image. These irrelevant noisy regions may have a negative effect on ConvNets that are trained with whole X-ray images. An attention guided ConvNet is described in [55] which learns where noisy regions are and uses that information to accurately identify regions showing the presence of a disorder. A heat map generated with an attention guided ConvNet serves as a guide to crop out the noisy regions of an X-ray image. KGZNet, a knowledge guided deep neural network for automatic diagnosis of thoracic diseases is proposed in [52]. KGZNet is a zoom neural network that is trained on hierarchically organized partitions of X-ray images and guided by human medical expertise. According to [52], thoracic diseases are typically limited within certain lung regions. A lung lesion is learnt through the analysis of lung images guided by an attention heat map. Disease-specific CAM attention heat maps focused on locating specific disorders were used to visualize suspicious lesions in chest X-ray images. A novel deep learning method to diagnose COVID-19 in chest X-ray images using a self-supervised learning approach with a convolutional attention module is presented in [51].