Breast Histopathology with High-Performance Computing and Deep Learning

Authors

  • Mara Graziani University of Applied Sciences of Western Switzerland, 3969 Sierre
  • Ivan Eggel University of Applied Sciences of Western Switzerland, 3969 Sierre
  • François Deligand INP-ENSEEIHT, 31000, Toulouse
  • Martin Bobák Institute of Informatics, Slovak Academy of Sciences, 845 07 Bratislava
  • Vincent Andrearczyk University of Applied Sciences of Western Switzerland, 3969 Sierre
  • Henning Müller University of Applied Sciences of Western Switzerland, 3969 Sierre

Keywords:

Histopathology, exascale, medical imaging, sampling

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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Published

2021-01-12

How to Cite

Graziani, M., Eggel, I., Deligand, F., Bobák, M., Andrearczyk, V., & Müller, H. (2021). Breast Histopathology with High-Performance Computing and Deep Learning. COMPUTING AND INFORMATICS, 39(4), 780–807. Retrieved from http://www.cai.sk/ojs/index.php/cai/article/view/2020_4_780

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Section

Special Section Articles

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