The German Oncology Center (GOC), a prominent medical center in Cyprus, sought to enhance its capabilities in analyzing large volumes of Magnetic Resonance Imaging (MRI) data, crucial for both patient care and medical research. The complexity and size of these datasets often exceeded the capacity of standard desktop or workstation computers, limiting the center’s ability to perform deep analyses efficiently. To address these challenges, GOC partnered with EuroCC at The Cyprus Institute to leverage High-Performance Computing (HPC) resources.
Through the collaboration, we provided GOC with access to state-of-the-art HPC systems and consultation on optimizing MRI data processing workflows. Key solutions included guidance on securing access to the HPC environment, setting up the infrastructure for multicore processing, and consulting on deep learning models for MRI data analysis using PyTorch.
A critical aspect of the consultation was setting up robust data transfer protocols to move the large MRI datasets to the HPC system securely and efficiently. We recommended utilizing Secure Copy Protocol (SCP) and rsync, ensuring smooth data synchronization without risk of loss or corruption. The introduction of parallel processing techniques enabled faster data preparation and analysis by distributing tasks across multiple cores, significantly reducing processing time. The consultation also focused on how to build a deep learning environment using PyTorch, enabling GOC to train and deploy models efficiently. With HPC resources, GOC was able to harness the power of GPU acceleration for deep learning, resulting in faster training cycles and more accurate model predictions.
This partnership led to substantial improvements, including optimized data transfer, efficient data loading and preprocessing, and enhanced resource utilization. These advancements have not only improved MRI data processing capabilities but have also positioned GOC at the forefront of medical imaging research, allowing for more precise diagnostics and better patient outcomes.
This success story highlights the transformative role of HPC in medical research, providing the computational power needed to handle the complexity of modern healthcare data and supporting future advancements in the field.`