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

Revision as of 13:30, 9 January 2017

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

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 T2 images containing cancer lesions and two image series which do not contain lesions. We plan to collect advice from others at the Project Week, select a deep learning framework, and attempt to build a classifer using this training data.	Initial testing with DIGITS 4 is underway. Preparing the dataset in the style of the MNIST example.

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 <!-- Approach and Plan bullet points -->
 * 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.
+* The dataset consists of a series of 50x50 Region Of Interest T2 images containing cancer lesions and two image series which do not contain lesions.
 * We plan to collect advice from others at the Project Week,  select a deep learning framework, and attempt to build a classifer using this training data.
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