PROCESS
Providing computing solutions for exascale challenges
CamNet Layer 3
A deep learning platform for digital histopathology. This 3rd layer focuses on network robustness and interpretability.
16
mentions
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contributor
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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).
Tags
Programming Language
- Python
License
- MIT
Source code
Participating organizations
Mentions
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Machine Learning in Medical ImagingJanuary 10, 2019
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Visualizing and Interpreting Feature Reuse of Pretrained CNNs for HistopathologyAugust 08, 2019
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Interpreting Intentionally Flawed Models with Linear ProbesOctober 22, 2019
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Improved interpretability for computer-aided severity assessment of retinopathy of prematurityMarch 13, 2019
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D2.1 Progress Report (Use Cases #1-5)October 31, 2018
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D2.2 Progress report (UC#1-5) - updateOctober 31, 2019
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D3.2 Performance modelling and prediction - progress reportOctober 31, 2019
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D4.1 Initial state of the art and requirements analysis, initial PROCESS architectureApril 30, 2018
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D4.3 Updated Requirements Analysis, Validation of 1st Prototype and Updated PROCESS ArchitectureApril 30, 2019
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D4.5 Validation of 2nd prototype and final PROCESS architectureJanuary 31, 2020
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D8.1 Validation Report of 1st PROCESS UpdateJuly 31, 2019
Projects with CamNet Layer 3
Contributors
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Mara GrazianiHaute École Spécialisée de Suisse Occidentale HES-SO Valais (HESSO)
Contact person
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