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

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Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
 
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Image:Cancer_roi_Img_00001.png|Example Cancer ROI
 
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Image:Cancer-roi-digits-training-0109.png|Initial LeNet training with DIGITS/Caffe
 
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Revision as of 13:36, 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 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.
  • created a DIGITS 4 Amazon instance using NVIDIA's marketplace image
  • Initial exploration with DIGITS 4 has begun. Preparing the dataset in the style of the MNIST example.

Background and References