# Benchmarking Anomaly Detection Methods for Extacardiac Findings in Cardiac MRI
## 4 Results and Discussion
### Unsupervised Image Reconstruction Methods  
Except for OS-DDPM [13], image reconstruction methods generally struggle to detect anomalies at the pixel level and, consequently, at the sample level. As shown in Figure 4, both AE [23] and VAE [36] produce blurred reconstructions due to the dimensionality  
Figure 3: Three sample test images from each subdataset: coronal (C), sagittal (S), and axial (A), and their respective GT. reduction applied to the feature space. While this reduction prevents the network from learning identity mappings or approximations thereof, it also compromises the quality of image representation learning. In addition, in the bottleneck layer, features are flattened into a 1D vector, eliminating spatial correlations. The loss function further exacerbates this blurring effect by not explicitly constraining the model to produce realistic outputs. These architectural constraints compel the model to focus on learning, within its capacity, latent representations that can be reliably decoded, resulting in the methods learning mean representations that balance accuracy while avoiding significant errors. Consequently, small lesions often disappear (sample S.I in Figure 5), whereas larger lesions induce distinct features unseen by the network and lead to reconstruction errors reflected in the anomaly maps (sample C.III in Figure 5). Curiously, VAE appears to generate slightly more blurred reconstructions than AE, which can be attributed to the Kullback-Leibler (KL) divergence regularization loss term and the stochastic nature of the bottleneck. These factors increase uncertainty during image generation, which may explain the lower pixel-AP values (\(-1.89\%\) on average across all views) observed with the VAE method (Table 2). However, training with a stochastic bottleneck may enhance robustness to hypothetical outliers, improving the model's ability to revert abnormal regions, as observed in the reconstruction of sample C.II (Figure 4). Despite this, the blurred reconstructions tend to emphasize high-frequency regions, making it difficult to detect abnormalities, as evidenced in all AE and VAE anomaly maps in Figure 5. Logically, this effect is less pronounced in low-frequency regions, such as the lungs, where the reconstruction task is less complex, leading to fewer false positives. Since most axial images predominantly capture the lungs, this explains the better performance of AE and VAE at both pixel and image levels in this view (Table 2). The restoration framework applied to VAE [14] was designed to enhance the outcome quality by iteratively modifying only the regions of the query image exhibiting a low likelihood of normality. As shown in Figure 4, this approach preserves normal regions more effectively, resulting in higher-quality reconstructions compared to AE and VAE. Additionally, Figure 4 also highlights the method's enhanced ability to correct abnormal regions. This combination of preserving normal areas and accurately addressing abnormalities led to significant gains in pixel-level classification performance compared to VAE, as reflected in Table 2 and Figure 5 (namely, in sample C.III). Overall, the anomaly maps reveal a reduction in false positives, particularly in high-frequency structures. However, the performance of r-VAE remains highly dependent on achieving an appropriate balance between gradient contributions of the reconstruction and normative terms--a process that is largely empirical. Furthermore, careful tuning of the restoration learning rate and the number of reconstruction steps is essential to prevent the generation of overly blurred or minimally altered restored images. Unlike previous methods, f-AnoGAN [38] generates reconstructions with finer detail and greater realism, as illustrated in Figure 4. Indeed, it is evident that all abnormalities were corrected and replaced by normal tissue.