How Resilient Are Deep Learning Models in Medical Image Analysis? The Case of the Moment-Based Adversarial Attack (Mb-AdA)

Security and Privacy in Machine Learning for Health Systems: Strategies and Challenges

OBJECTIVES

Machine learning (ML) is a powerful asset to support physicians in decision-making procedures, providing timely answers. However, ML for health systems can suffer from security attacks and privacy violations. This paper investigates studies of security and privacy in ML for health.

METHODS

We examine attacks, defenses, and privacy-preserving strategies, discussing their challenges. We conducted the following research protocol: starting a manual search, defining the search string, removing duplicated papers, filtering papers by title and abstract, then their full texts, and analyzing their contributions, including strategies and challenges. Finally, we collected and discussed 40 papers on attacks, defense, and privacy.

RESULTS

Our findings identified the most employed strategies for each domain. We found trends in attacks, including universal adversarial perturbation (UAPs), generative adversarial network (GAN)-based attacks, and DeepFakes to generate malicious examples. Trends in defense are adversarial training, GAN-based strategies, and out-of-distribution (OOD) to identify and mitigate adversarial examples (AE). We found privacy-preserving strategies such as federated learning (FL), differential privacy, and combinations of strategies to enhance the FL. Challenges in privacy comprehend the development of attacks that bypass fine-tuning, defenses to calibrate models to improve their robustness, and privacy methods to enhance the FL strategy.

CONCLUSIONS

In conclusion, it is critical to explore security and privacy in ML for health, because it has grown risks and open vulnerabilities. Our study presents strategies and challenges to guide research to investigate issues about security and privacy in ML applied to health systems.

Improving the Age Estimation Efficiency by Calculation of the Area Ratio Index Using Semi-Automatic Segmentation of Knee MRI Images

The knee is an anatomical structure that can provide a great deal of data for research on age estimation. The aim of this study was to evaluate and apply a method for semi-automatic measurements of the area under the growth plate closure of the femur distal epiphysis and the growth plate closure itself on the 2D coronary slices using T2 weighted images (T2WI) generated on magnetic resonance (MRI) devices of different technical and technological characteristics. After the semi-automatic segmentation of the femur distal epiphysis under the growth plate closure and the growth plate closure itself, the areas of the measured closures were calculated using MATLAB version: 9.12. (R2022a), MathWorks Inc., Natick, MA, USA, for each individual coronal slice. The area ratio index (ARI) was calculated as the ratio between the area under the growth plate closure of the femur distal epiphysis and the growth plate closure itself. The study sample consisted of 27 female and 23 male Caucasian participants aged 10 to 26 years. A total of 339 T2WI images were used for ARI calculations. There was a positive correlation between chronological age and the average ARI measured by three independent observers (r = 0.8280, p < 0.001). Multiple regression analysis did not show any significant impact of the technical and technological characteristics of the MRI devices on ARI. The results of this study showed that ARI could serve as a useful tool for age estimation using knee MRI as well as for the further development of artificial intelligence (AI) applications.