CamNet Layer 3

A deep learning platform for digital histopathology. This 3rd layer focuses on network robustness and interpretability.

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35 commits | Last update: November 21, 2020

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What CamNet Layer 3 can do for you

  • provide interpretability to the classification results of CamNET Layer 2
  • improves insight into the behaviour of the neural network
  • increases trust in the results

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.

This 3rd layer focuses on determining the robustness and interpretability of the convolutional neural network trained by layer 2. This is an important feature, as the physicians need to understand why the network labels certain patches as containing tumors (or not).

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Tags
  • Medical image data
  • Image processing
  • Machine learning
  • User interfaces
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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