Difference between revisions of "2017 Winter Project Week/Evaluate Deep Learning for binary cancer legion classification"
From NAMIC Wiki
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* Start with a dataset, prepared at the Frederick National Lab for Cancer Research, to use to train a classifier. | * 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 images containing cancer lesions and two image series which do not contain lesions. | ||
− | * We plan to collect advice from others at the Project Week | + | * 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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<!-- Progress and Next steps bullet points (fill out at the end of project week) --> | <!-- Progress and Next steps bullet points (fill out at the end of project week) --> | ||
− | * | + | * Initial testing with DIGITS 4 is underway. Preparing the dataset in the style of the MNIST example. |
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==Background and References== | ==Background and References== | ||
<!-- Use this space for information that may help people better understand your project, like links to papers, source code, or data --> | <!-- Use this space for information that may help people better understand your project, like links to papers, source code, or data --> |
Revision as of 12:57, 9 January 2017
Home < 2017 Winter Project Week < Evaluate Deep Learning for binary cancer legion classificationKey Investigators
- Curt Lisle, KnowledgeVis, LLC
- others are invited
Project Description
Objective | Approach and Plan | Progress and Next Steps |
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