CamNet Layer 1

CamNET is a deep learning platform for digital histopathology. This 1st layer provides the data pre-processing and patch extraction functionality.

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36 commits | Last update: June 08, 2020

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What CamNet Layer 1 can do for you

  • Whole slide images are loaded and preprocessed with Otsu thresholding. Patches of normal and tumor tissue can be extracted in a parallel distributed fashion
  • The patches can be extracted at different resolution levels with a random sampling scheme or uniform dense coverage can be selected
  • Intermediate dabases of tissue patches are generated on the local storage
  • State of the art convolutional neural networks can be trained/finetuned on the histopathology data
  • Distributed training on multiple GPUs is also available

CamNET is a deep learning platform for digital histopathology aimed at detecting tumor tissue in gigapixel scans of slides containing breast lymph nodes tissue. Currently, a 'human' expert needs to inspect these slides in order to find suspected tumors, which is very labor intensive. CamNET attempts to automate this using a deep learning approach. CamNET software is organized into three layers.

This first layer implements the extraction of 224x224 pixel patches from the original gigapixel slides of breast lymph nodes tissue. These patches are randomly sampled from the slide, which is annotated by a physician to point out the tumor areas. Patches belonging to a tumorous region are assigned a ‘tumor’ label (a Boolean variable equals to True). The extracted data are stored in an intermediate dataset with the corresponding labels.

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Tags
  • Medical image data
  • Image processing
  • Machine learning
Programming Language
  • Python
License
  • MIT
Source code

Participating organizations

Contributors

  • Mara Graziani
    Haute École Spécialisée de Suisse Occidentale HES-SO Valais (HESSO)
Contact person
Mara Graziani
Haute École Spécialisée de Suisse Occidentale HES-SO Valais (HESSO)

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