# Benchmarking Anomaly Detection Methods for Extacardiac Findings in Cardiac MRI
## 5 Conclusions  
This study presents a comprehensive comparative analysis of unsupervised, semi-supervised, and open-set supervised anomaly detection methods for identifying ECFs in CMR HASTE images. The results reveal that while some state-of-the-art AD methods achieved performance comparable to fully supervised baselines on certain metrics, the latter remained the most reliable option for this challenging dataset. The suboptimal performance of AD methods may be attributed to the high variability and structural complexity of these images. Methods incorporating abnormality supervision, which prioritize learning abnormal patterns over normal variability, are less affected by these challenges but still struggle to generalize to rare or unseen anomalies.  
Among the tested approaches, OS-DDPM, DAE, BGAD, DRA, SupIC, and SupIS demonstrated the highest effectiveness in detecting ECFs. However, the performance of supervised methods remains highly dependent on the availability of abnormal training samples. In scenarios with limited abnormal data, unsupervised AD strategies exhibited stronger relative performance. The plateaued performance of fully supervised methods, coupled with the promising results of certain unsupervised AD approaches, underscores the potential for further exploration and refinement of AD strategies in this clinical task.  
**Author Contributions:** Conceptualization, S.Q.; methodology, E.P. and S.Q.; software, E.P.; validation, J.C.F. and S.Q.; formal analysis, E.P. and S.Q.; investigation, E.P. and S.Q.; resources, V.H.P., J.C.F. and S.Q.; data curation, P.M.C., C.S. and V.H.P.; writing--original draft preparation, E.P. and S.Q.; writing--review and editing, E.P., P.M.C., C.S., V.H.P., J.C.F. and S.Q.; supervision, J.C.F. and S.Q.; project administration, S.Q.; funding acquisition, S.Q. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Portuguese National funds, through the Foundation for Science and Technology (FCT)--projects UIDB/50026/2020 (DOI: 10.54499/UIDB/50026/2020), UIDP/50026/2020 (DOI: 10.54499/UIDP/50026/2020), LA/P/0050/2020 (DOI: 10.54499/LA/P/0050/2020), and PTDC/EMD-EMD/1140/2020 (DOI: 10.54499/PTDC/EMD-EMD/1140/2020), and grant CEECIND/03064/2018 (S.Q.; DOI: 10.54499/CEECIND/03064/2018/CP1581/CT0017), and by the project NORTE-01-0145-FEDER-000039, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (ERDF).  
**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Hospital of Braga (Braga, Portugal; ref. 180/2023). This is a retrospective study with no intervention. No clinical information was extracted and the images were exported in a pseudoanonymized format without direct identifiers.  
**Informed Consent Statement:** Patient consent was waived due to the retrospective nature of the study and the use of pseudoanonymized imaging data.  
**Data Availability Statement:** The dataset used in this study is not publicly available due to restrictions imposed by the Ethics Committee. Requests for access can be directed to the corresponding author and will be considered on a case-by-case basis.  
**Acknowledgments:** The authors would like to acknowledge the Clinical Academic Center-Braga (2 CA-Braga) for its support in submitting the project to the ethics committee of the Hospital of Braga (HB) and facilitating the retrospective collection of image data from HB. Additionally, the authors acknowledge the donation of an RTX A6000 GPU by NVIDIA Corporation (USA).  
**Conflicts of Interest:** The authors declare no conflicts of interest.