AI and HPC Convergence for Medical Image Analysis
Efficient deep learning is endorsed for the solution of different problems in medical diagnosis such as enhancement, segmentation, and registration. Nowadays, developing distributed systems for deep learning in medical diagnosis requires analysis of huge amount of data for the training. Under such circumstances, efficient usage of distributed HPC systems avoids delay emerging in training procedure and reduces the overall time. The talk will discuss the impact of the application of AI methods for medical image analysis in distributed HPC systems. The talk will illustrate how these two main fields AI and HPC can be converged together for the real time medical image diagnosis using distributed GPU systems.
Dr. Nitin Satpute is a Postdoc Researcher in Machine Learning and Computational Intelligence at Aarhus University (AU), Denmark. Before joining AU, he was a Marie Curie researcher under HiPerNav project funded by European Union (EU) at University of Cordoba, Spain where he has been conferred PhD in Computer Science. He has done his Master of Engineering (ME) in Embedded Systems from BITS Pilani, India. He is currently involved in the fast efficient distributed training for Deep Neural Networks (DNN). His research interest includes AI, parallel computing, medical image analysis and automated crowd management system.