# Benchmarking Anomaly Detection Methods for Extacardiac Findings in Cardiac MRI
## 2 Related Work
models operate via a Markovian process of noising and denoising [13; 22; 39]. In the AD domain, [PERSON] et al. proposed anoDDPM [22], which employs a partial diffusion process to selectively correct regions deviating from the learned distribution rather than generating entirely new samples. Additionally, rather than relying on Gaussian noise, they utilize Simplex noise as it corrupts primarily low-frequency components, and consequently abnormalities, preserving high-frequency structures that act as landmarks during the reverse process. To further enhance reconstruction quality and reduce false positives, [PERSON] et al. proposed patched DDPM (pDDPM), where a partial diffusion process is applied to patches rather than to the entire image [13]. Since this approach is computationally demanding, they opted for a one-step prediction framework, leveraging contextual information from surrounding patches to improve anatomical consistency. Image reconstruction methods often struggle with edges and complex textures. To address these challenges, feature modeling methods have emerged, focusing on detecting anomalies in the feature space rather than directly in the image space [19; 31]. These methods can be further categorized into two subgroups: those based on reconstruction errors [19; 26; 29; 31] and those relying on statistical modeling of feature maps [25; 28]. [PERSON] et al. [31] introduced the deep feature reconstruction (DFR) method, which extracts hierarchical features from a pre-trained network, aligns and resizes them to match the input size, and applies mean filtering to form a single multi-scaled feature volume. This volume is then processed through an AE with \(1\times 1\) convolutions. Similarly, [PERSON] et al. [19] proposed the feature autoencoder (FAE) method, which employs an AE with a larger kernel size and a structural similarity index measure (SSIM) loss to improve robustness compared to simple pixel-wise differences. However, unlike DFR, FAE does not incorporate feature map alignment or mean filtering. Teacher-student models are another way of using feature reconstruction errors to detect anomalies. [PERSON] et al. [26] proposed a reverse distillation (RD) model, utilizing heterogeneous teacher and student networks along with a one-class bottleneck embedding module to enforce the networks to learn distinguishable filters. The anomaly map is computed as the averaged difference between feature maps from corresponding layers of the teacher and student networks. Notably, all these feature modeling methods rely on frozen pre-trained backbones for feature extraction. In contrast, [PERSON] et al. [29] introduced ReContrast, an approach that optimizes the entire network to better align with the target domain. This is achieved through a contrastive learning paradigm, where a shared decoder is paired with both an unfrozen encoder trained on the target domain and a frozen one from the pre-training domain. The loss is computed between the representations of both encoders but is backpropagated only through the target-domain encoder. Departing from these approaches, [PERSON] et al. [25] proposed PaDiM, which models normal features as a multivariate Gaussian distribution and detects abnormal regions based on the Mahalanobis distance between image features and this distribution. Additionally, some methods have sought to model normal data with more complex statistical frameworks. [PERSON] et al. [28] introduced CFLOW-AD, which applies a sequence of bijective transformations to a simpler distribution using normalizing flows, enabling the generation of more complex data distributions. As an alternative to detecting anomalies in AE models, attention-based methods leverage attention maps. [PERSON] and [PERSON] [30] proposed the explanatory variational autoencoder (expVAE), which localizes anomalies by computing gradient-based attention maps under the assumption that the posterior distribution of normal samples matches the prior distribution. Later, [PERSON] et al.