# Benchmarking Anomaly Detection Methods for Extacardiac Findings in Cardiac MRI
## 4 Results and Discussion  
The test set results for the coronal, sagittal, and axial subdatasets are summarized in Table 2, representing the average across the five subdataset versions. To improve metric interpretability, the expected performance of a random classifier is also included. For a comprehensive qualitative evaluation, three sample images from each subdataset were selected based on the presence of anomalies varying in size, shape, and contrast, as well as their location across different regions of the chest. These images, which also illustrate the inherent variability of normal anatomy, are shown in Figure 3 and will be referenced throughout this section.  
In general, all AD methods--except for DAE [16] and OS-DDPM [13] in the coronal and sagittal subdatasets, respectively--achieved lower image-level classification performance compared to the supervised baselines. However, the performance of DAE [16] and OS-DDPM [13] did not exhibit statistically significant differences from either supervised baseline. Notably, several methods, including r-VAE [14], FAE [19], RD [26], ReContrast [29], PII [24], DAE [16], and BGAD [33], achieved lower average image-level results but showed no statistically significant differences from the supervised baselines for at least one subdataset. At the pixel level, OS-DDPM [13], FAE [19], ReContrast [29], PII [24], and BGAD [33] also demonstrated no statistically different performance from the baselines.  
Importantly, the performance of the supervised baselines is strongly influenced by the proportion of abnormal images available for training. When the number of abnormal samples was reduced to 50%, 25%, and 10% of those available in the training subset, their performance decreased significantly, particularly for the SupIC method (Table 2). Under these conditions, several top-performing unsupervised AD methods surpassed the baselines. While these results highlight the dependence of supervised methods on abnormal training data, they also suggest that, in this complex dataset, supervised learning of abnormal patterns remains generally more effective than current state-of-the-art AD approaches. Despite this, their performance remains insufficient for clinical application. Indeed, after selecting the optimal threshold on the validation subset by maximizing the difference between the true positive and false positive rate (Youden's index) and applying it to the test subset, the SupIC and SupIS methods achieved averaged sample-level F\({}_{1}\)-scores of 64.75% and 59.01%, respectively, across all subdatasets. In terms of sample-level specificity and recall, SupIC attained 78.36% and 59.19%, while SupIS achieved 86.63% and 48.93%, respectively. These results are consistent with the findings of the only previous study in this clinical application [5].  
A comparison of the methods' performance across the three subdatasets reveals that, in general, the methods perform best on the axial subdataset and comparatively worse on the sagittal subdataset. This discrepancy may be attributed to structural complexity differences across views. Unlike coronal and sagittal slices, axial slices capture a narrower range of anatomical structures, making it easier to learn normal patterns. Notably, methods incorporating some form of supervision exhibit even greater improvement in the axial subdataset. This advantage may stem from the more limited spectrum of abnormalities in axial slices, where lungs are the most prominently represented structure.  
The following subsections provide a detailed analysis of the results by category.