We are happy to share that our papers are published in the special issue of Journal of Artificial Intelligence Theory and Applications.

In "Unfairness of Deep Learning Methods Arising Gender Bias in COVID-19 Diagnosis of Medical Images", we explored the effect of gender imbalance in medical imaging dataset when applying deep learning models to detect Covid-19. We used a large enough and balance dataset in terms of COVID and non-COVID images of adults from different data sources. (ResearchGate link: https://www.researchgate.net/publication/352055329_Unfairness_of_Deep_Learning_Methods_Arising_Gender_Bias_in_Covid-19_Diagnosis_of_Medical_Images#fullTextFileContent
Publication link: https://aita.bakircay.edu.tr/Yuklenenler/AITA/A21_02_002.pdf )

In "Towards Federated Learning in Prediction of Medical Images: A Case Study", we studied Federated Learning framework on distributed data across multiple clients with no sharing of the patient data. We compared the performance of Federated Learning model against the centralized learning model and reported the promising results to sensitive medical data in terms of privacy and security. (ResearchGate link: https://www.researchgate.net/publication/352055238_Towards_Federated_Learning_in_Identification_of_Medical_Images_A_Case_Study
Publication link: https://aita.bakircay.edu.tr/Yuklenenler/AITA/A21_02_002.pdf)