## 4 Discussion
Incorporating findings from [PERSON] et al. [61], the application of saliency methods like Grad-CAM in medical imaging appears to demand scrutiny, particularly in their role in diagnostic decision-making. The authors conducted a comprehensive evaluation of seven saliency methods, including Grad-CAM, across diverse neural network architectures. A notable aspect of their study was the establishment of the first human benchmark for chest X-ray segmentation in a multilabel classification context. The study's results indicated that while Grad-CAM was generally more effective than its counterparts in highlighting pathologies, all the evaluated saliency methods were more prone to fail in localizing important pathologies compared to human experts. This gap in performance was particularly evident in cases of pathologies that were smaller and had more complex shapes. Additionally, the research highlighted a positive correlation between the confidence level of the model and the accuracy of Grad-CAM in localizing pathologies. These findings emphasize the need for caution and further research before considering the integration of saliency methods like Grad-CAM into real-world clinical settings. From our results, one high-performing set involved rotation, which may be partly attributed to the rotated orientation of the knee joint along the vertical axis of the radiograph image. In this configuration, the convolution operation repeatedly encounters relevant features as it slides across the image, potentially leading to more condensed and effective feature maps. This is in contrast to a non-rotated radiograph image, where the knee joint occupies only a single vertical segment of the image. In this latter case, the convolution would likely traverse the entire joint just once or twice, depending on the receptive field, making feature extraction potentially slightly less effective. However, we previously discussed how the 'baseline' configuration enhanced recall values, which is a significant factor, especially in scenarios where missing true positives is costly. By utilizing the baseline modality, we may achieve a more evenly distributed performance across the relevant classes, ensuring that the system is not only accurate but also consistently reliable in identifying true instances across different categories. Integrating adversarial augmentations into the study of various diseases offers considerable advantages. For instance, selectively omitting regions of interest (ROIs)--whether through dynamic clustering algorithms or predetermined for specific diseases--might reveal potential confounds, especially if the model's key performance indicators remain stable despite these alterations. Adding pixel noise is another strategic augmentation that serves two critical functions: it underscores potential disruptions in the classification process and uncovers specific sensitivities of the employed architecture to noise. This insight is particularly valuable; for example, if a solution demonstrates extreme sensitivity to noise, it could inform the decision to incorporate affine blurring transformations during the training phase to mitigate this issue. Such strategies not only bolster the robustness of the models but also deepen our understanding of disease detection and classification, thereby enhancing the precision and reliability of medical imaging analyses. In assessing the underperformance of certain methods, particularly the horizontal split approach, it becomes apparent that this technique may inherently dilute the informativeness of the image data. In knee radiography, pathological changes are often more pronounced on one side of the knee joint. When the image is split horizontally, one half may carry less informative features but is still labeled similarly to the more informative half. This dilution of label information could be a significant factor contributing to the reduced efficacy of this method.