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Pathology Image Data Lake

Pathology Image Data Lake for Analytics Knowledge & Education (PathLAKE)

Research Goal :

Pathology Image Data Lake for Analytics Knowledge & Education (PathLAKE) will deliver high-impact exemplar projects reflecting today’s demand for AI-driven diagnostics to increase efficiency in pathology reporting and improve patient outcomes through advanced diagnostics and selection of patients for personalised medicine. PathLAKE will play a leading role in the development, validation and implementation of AI in cellular pathology. It will be an invaluable resource for researchers and UK industry, enabling a step change in the understanding of disease and the provision of patient healthcare.

Kelvin Usage :

Developing AI models (e.g. Machine Learning, Deep Learning) for the PathLAKE project need high performance computing (HPC) with large GPU memories.For this the group used the 60 x AMD compute nodes 128 core (4 x high memory nodes (2TB) and 32 x NVIDIA Tesla v100 GPUs  to develop any AI models for PathLAKE project to deploy the models in cellular pathology. Moreover, training Deep Learning (DL) models needs a long time period (~a week) which was supported by Kelvin2 job scheduler system. Finally, the group made use of the central software repository by accessing and using DL related software (e.g., Pytorch) which allowed the developers to generate DL models efficiently.