Ensuring Trustworthy Medical Artificial Intelligence through Ethical and Philosophical Principles

no code implementations • 23 Apr 2023 • Debesh Jha, Ashish Rauniyar, Abhiskek Srivastava, Desta Haileselassie Hagos, Nikhil Kumar Tomar, Vanshali Sharma, Elif Keles, Zheyuan Zhang, Ugur Demir, Ahmet Topcu, Anis Yazidi, Jan Erik Håakegård, Ulas Bagci

Artificial intelligence (AI) methods hold immense potential to revolutionize numerous medical care by enhancing the experience of medical experts and patients.

Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

no code implementations • 5 Aug 2022 • Ashish Rauniyar, Desta Haileselassie Hagos, Debesh Jha, Jan Erik Håkegård, Ulas Bagci, Danda B. Rawat, Vladimir Vlassov

To this end, in this paper, we explore the present and future of FL technology in medical applications where data sharing is a significant challenge.

Federated Learning

COROID: A Crowdsourcing-based Companion Drones to Tackle Current and Future Pandemics

no code implementations • 19 Jul 2022 • Ashish Rauniyar, Desta Haileselassie Hagos, Debesh Jha, Jan Erik Håkegård

Therefore, we believe that the COROID drone is innovative and has a huge potential to tackle COVID-19 and future pandemics.

Accelerate Model Parallel Training by Using Efficient Graph Traversal Order in Device Placement

1 code implementation • 21 Jan 2022 • Tianze Wang, Amir H. Payberah, Desta Haileselassie Hagos, Vladimir Vlassov

In particular, we empirically study how different graph traversal order leads to different device placement, which in turn affects the training execution time.

Decision Making