Difference between revisions of "2017 Winter Project Week/Evaluate Deep Learning for binary cancer legion classification"

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* Curt has a dataset prepared by colleagues at the Frederick National Lab for Cancer Research to use to train a classifier.  
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* Start with a dataset, prepared at the Frederick National Lab for Cancer Research, to use to train a classifier.  
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* The library consists of a set of 50x50 PNG images containing cancer lesions and two different 50x50 sets which do not contain lesions.  
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* The dataset consists of a series of 50x50 Region Of Interest images containing cancer lesions and two image series which do not contain lesions.  
 
* We plan to collect advice from others at the Project Week to select a deep learning framework and attempt to build a classifer using this training data.
 
* We plan to collect advice from others at the Project Week to select a deep learning framework and attempt to build a classifer using this training data.
 
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==Background and References==
 
==Background and References==
 
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Revision as of 04:03, 9 January 2017

Home < 2017 Winter Project Week < Evaluate Deep Learning for binary cancer legion classification

Key Investigators

Curt Lisle, KnowledgeVis, LLC others are invited

Project Description

Objective Approach and Plan Progress and Next Steps
  • Train a neural network to become a binary classifier for the detection of cancer lesions using T2 ROI images
  • Start with a dataset, prepared at the Frederick National Lab for Cancer Research, to use to train a classifier.
  • The dataset consists of a series of 50x50 Region Of Interest images containing cancer lesions and two image series which do not contain lesions.
  • We plan to collect advice from others at the Project Week to select a deep learning framework and attempt to build a classifer using this training data.

Background and References

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