## 3 Results
### XAI Functions
#### 3.6.1 Visualization
Feature attribution approaches identify and quantify the contribution of individual features or regions in the input data to the output prediction. The region of evidence (ROE) method described by [PERSON] et al. (2021) focuses on highlighting discriminative regions (ROEs) in ultrasound images that the CNN relies on for making class decisions, aligning with feature attribution through visualization [40]. The Explainer framework designed by [PERSON] et al. (2023) generates heat maps, or \"E maps\", from the convolution of feature maps to highlight the region a CNN model uses for determining the output class [49]. These E maps, which have the same dimensions as the input feature maps, assign values between 0 and 1  
Figure 9: Visualization examples for GIST, leiomyoma, and pancreatic rest tumors with the Grad-CAM plots generated by different methods reflecting the decision basis of different models. The first column presents the original US image. The 2 nd column shows the expert annotations. The 3 rd–6 th columns present the generated Grad-CAM saliency maps using a baseline model, a multiattribute guided network (MAG), a contextual attention network (CA), and a combined MAG–CA network. Adapted from [PERSON] et al. (2024) [53], with permission from Elsevier. to represent the importance of each pixel. Subsequently, these E maps are multiplied by the feature maps, with their resultant map used for the classification. This method produces salient mappings directly aligned with the model's reasoning process, providing intrinsic explanations as it leverages the model's internal architectures and computations to attribute features directly contributing to the output. Pixels deemed unimportant are assigned a value of 0, effectively excluding the features they represent from the classification. This approach contrasts with Grad-CAM, which identifies significant features considered by the model, whereas the Explainer focuses on the features the model actually uses to make predictions [49]. Although there may be overlap, these two sets of features are not identical. Additionally, the Explainer framework reshapes feature maps from different layers into the same size, allowing it to combine inputs from multiple layers rather than being limited to the last convolution layer. In segmentation tasks, integrated visualization approaches empower visual evaluations of the model's ability to separate different regions (tumor vs. healthy tissue) within the images. Integrated visualization techniques enhance explainability without perturbing the input or analyzing backpropagation gradients directly, as they inherently integrate visualization into the model's architecture. The XNML method integrates data-centric learning and causal explanations directly into the learning process. It focuses on generating explainable ML predictions in medical anomaly detection by generating color-coded masks ranking lesion candidates [45]. In [PERSON] et al. (2023), the developed MTL approach combined classification, segmentation, and quantitative explainability using semantic Shapley values [43]. Their MT-BI-RADS model outputs the predicted category of each BI-RADS risk assessment descriptor and provides visual explanations by highlighting tumor regions with image segmentation. In localization tasks, object detection frameworks localize and highlight critical areas (lesions) in an image using backpropagation-based heat maps that allow for visual interpretation by experts. The activation heat map in [PERSON] et al. (2023) was generated from global image features [38]. This method involves backpropagation of gradients from the  
Figure 10: Input of the (**a**) original input image and (**b**) radiologist-highlighted region of hypoechoic lesion with mixed echogenicity prostate for malignant case, compared to the resultant (**c**) simulated image by LIME, which initially locates the regions that could be worthy of investigation given the input image, and (**d**) the final generated image by LIME explaining why the case was classified as malignant.