## 3 Results
### XAI Functions
#### 3.6.2 Semantics  
XAI methods involving semantics aim to produce meaningful descriptions or labels that correlate closely with the features and decision-making criteria used by clinical experts. This approach enhances the explainability of AI systems by providing explanations that are not only technically accurate but are also understandable and clinically relevant. The identified semantics methods present feature-based or transparency-based approaches and are all applied in classification tasks. Feature-based approaches emphasize individual or groups of features that substantially influence model predictions. These semantic explanations provide insights into the intrinsic decision-making processes of models by correlating computational assessments with clinically relevant features. The bag-of-features (BoF) method transforms complex image data into discrete features, known as \"visual words\", simplifying the interpretation of transformer models' decisions. The method segments images into local patches and identifies recurrent patterns, creating a vocabulary of visual words that represent clusters of similar patches. These visual words are then used to generate histograms that serve as input to classifiers, allowing models to make predictions based on the distribution of these words. This approach not only simplifies image data but also aligns closely with clinical interpretations, focusing on patterns and textures indicative of pathological conditions [38]. Developed by [PERSON] et al. (2021), BI-RADS-Net employs BI-RADS descriptors such as shape, margins, and densities as features to elucidate their impact on tumor malignancy classifications. This method leverages standardized clinical lexicons, enhancing the transparency and applicability of AI in clinical settings [52]. The SHAP (Shapley additive explanations) method quantifies the contribution of each input feature to model predictions using Shapley values, a concept from cooperative game theory, to ensure fair and consistent explanations across all possible combinations of feature inputs. SHAP has been applied in various studies to provide quantitative, post hoc explanations. In [PERSON] et al., SHAP showed the contribution of each BI-RADS descriptor in MTL breast cancer classifications, offering insights into the significance of ultrasound image features like shape and margin [43]. [PERSON] et al. (2023) utilized SHAP to identify key features in dermatoscopic images most indicative of malignancy or benignity, highlighting the diagnostic importance of specific visual patterns [44]. [PERSON] et al. (2022) applied SHAP to an ensemble model for breast cancer diagnosis, detailing how statistical texture features impact malignant or benign class predictions [48], see Figure 12. These associations supported more informed clinical decisions. Figure 11: Generation of the ROI and the local patches from the images using the global features. (**a**) The original image to be used as the input for the global branch. (**b**) The generated activation heat map of the features. (**c**) The binarized heat map and the bounding box spanning it. (**d**) The cropped local patch to be used as the input to the local branch. Adapted from [PERSON] et al. (2023) [38], with permission from Elsevier. Transparency-based approaches are designed to be inherently explainable. They may also enhance existing models with additional mechanisms that clarify their internal decision processes. These approaches ensure that the features learned by the model are clinically meaningful and do not just represent simulated correlations or artifacts of the training data. The image similarity technique (AIBx) in [PERSON] et al. (2020) integrated clinically significant patterns and semantic descriptors into AI models. The AI predictions were aligned with medical standards by embedding diagnostic categories, molecular markers, treatment responses, and recurrence status directly into the decision-making process [51].