# Benchmarking Anomaly Detection Methods for Extacardiac Findings in Cardiac MRI
## 4 Results and Discussion
### Unsupervised Feature Modeling Methods  
The results in Table 2 corroborate a previous observation in the literature [18] that AD strategies applied to image features, rather than raw pixel data, are generally more effective. This is likely because feature-based approaches provide a clearer mechanism for learning to distinguish normal and abnormal patterns. Both DFR [31] and FAE [19] are AE-based methods that utilize feature volumes as input instead of raw pixels. Notably, the anomaly maps generated by FAE exhibit greater discriminability than those produced by DFR, as observed in samples C.II, C.III, S.II, and A.I (Figure 6). Two primary factors may explain this difference: (1) the mean filter applied to the hierarchical feature volume before it is fed into the AE network; (2) the use of \(1\times 1\) convolutions in the AE. According to the authors of DFR [31], the mean filter smooths large transitions across features, improving robustness to noise. However, this process may also obscure small abnormal features within the feature volume. In turn, the \(1\times 1\) convolutions might limit the network's ability to learn the broader representation of the query features, reducing its discriminative capacity. These factors suggest that FAE's architecture is better suited for capturing and preserving the subtle details necessary to distinguish abnormalities, resulting in superior anomaly maps and classification performance (+5.06%, +5.10%, and +5.00%, respectively, in pixel-AP, sample-AUROC, and sample-AP across the three views).  
RD [26] and ReContrast [29] are also feature reconstruction-based methods and have achieved interesting pixel-level and image-level classification results compared to other unsupervised approaches (Table 2). Unlike DFR and FAE, these methods compute residuals between shallow feature maps using a teacher-student network. This strategy produces  
Figure 6: Anomaly maps generated by feature modeling methods for the samples shown in Figure 3.  
anomaly maps with finer details, but it also makes them more susceptible to small noisy reconstruction errors. While such sensitivity can be beneficial for localizing small abnormalities, it complicates the accurate delineation of larger lesions, as seen in the anomaly map for sample C.III in Figure 6. The modifications introduced by [PERSON] et al. [29] resulted in improved pixel-level performance (+4.49% across all views), demonstrating that proper backbone fine-tuning can enhance feature discriminability for modeling normality. The positive-pair contrastive learning strategy, combined with the stop-gradient operation, also proved effective in boosting the performance of a vanilla teacher-student network. Notably, the anomaly maps produced by ReContrast appear more blurred compared to those from RD, likely due to the Gaussian filter applied during post-processing.  
Compared to the aforementioned feature modeling methods, PaDiM [25] and CFLOW-AD [28] exhibit inferior performance (Table 2). Surprisingly, PaDiM outperforms CFLOW-AD, as noted in Figure 6 and Table 2 (+1.51%, +7.17%, and +6.54%, respectively, in pixel-AP, sample-AUROC, and sample-AP across the three views). Given the high anatomical variability in our dataset, one would expect the simple Gaussian distribution employed by PaDiM to be insufficient to effectively model the distribution of the extracted features. Instead, methods like CFLOW-AD, which learn more complex distributions from the data, were anticipated to yield superior results. However, this expectation was not met in practice.