Sparse coding of pathology slides compared to transfer learning with deep neural networks

Sparse coding of pathology slides compared to transfer learning with deep neural networks

Abstract Background Histopathology images of tumor biopsies present unique challenges for applying machine learning to the diagnosis and treatment of cancer. The pathology slides are high resolution, often exceeding 1GB, have non-uniform dimensions, and often contain multiple tissue slices of varyin...

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Bibliographic Details
Main Authors: Will Fischer, Sanketh S. Moudgalya, Judith D. Cohn, Nga T. T. Nguyen, Garrett T. Kenyon
Format: Article
Language:English
Published: BMC 2018-12-01
Series:BMC Bioinformatics
Subjects:
Cancer pathology slides
TCGA
Sparse coding
Locally Competitive Algorithm
Unsupervised learning
Transfer learning
Online Access:http://link.springer.com/article/10.1186/s12859-018-2504-8

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http://link.springer.com/article/10.1186/s12859-018-2504-8

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