Abstract

The increasingly intensive collection of digitalized images of tumor tissue over the last decade made histopathology a demanding application in terms of computational and storage resources. With images containing billions of pixels, the need for optimizing and adapting histopathology to large-scale data analysis is compelling. This paper presents a modular pipeline with three independent layers for the detection of tumoros regions in digital specimens of breast lymph nodes with deep learning models. Our pipeline can be deployed either on local machines or high-performance computing resources with a containerized approach. The need for expertise in high-performance computing is removed by the self-sufficient structure of Docker containers, whereas a large possibility for customization is left in terms of deep learning models and hyperparameters optimization. We show that by deploying the software layers in different infrastructures we optimize both the data preprocessing and the network training times, further increasing the scalability of the application to datasets of approximatively 43 million images. The code is open source and available on Github.

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Cite this article

@article{cai-5327,
  author  = {Mara Graziani and Ivan Eggel and François Deligand and Martin Bobák and Vincent Andrearczyk and Henning Müller},
  title   = {Breast Histopathology with High-Performance Computing and Deep Learning},
  journal = {Computing and Informatics},
  volume  = {39},
  number  = {4},
  pages   = {780--807},
  year    = {2020},
  issn    = {1335-9150},
  doi     = {10.31577/cai_2020_4_780}
}
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