Scalable and Fault-Tolerant Distributed Training of Deep Learning Models for Medical Image Analysis on HPC Clusters

Deep learning models have demonstrated significant potential in medical image analysis. However, training these models on large datasets requires substantial computational resources, often necessitating the use of High-Performance Computing (HPC) clusters. This paper investigates strategies for scalable and fault-tolerant distributed training of deep learning models for medical image analysis on HPC environments. We explore various data parallelism techniques, communication optimization strategies, and fault tolerance mechanisms, analyzing their impact on training performance and robustness. Furthermore, we address the critical role of data quality and annotation consistency, drawing upon recent advancements in robust data annotation techniques to mitigate the effects of noisy or inconsistent labels on model performance.