Representation learning for mammography mass lesion classification with convolutional neural networks

@article{Ovalle2016RepresentationLF,
  title={Representation learning for mammography mass lesion classification with convolutional neural networks},
  author={John Edison Arevalo Ovalle and Fabio A. Gonz{\'a}lez and Ra{\'u}l Ramos-Poll{\'a}n and Jos{\'e} Lu{\'i}s Oliveira and Miguel {\'A}ngel Guevara-L{\'o}pez},
  journal={Computer methods and programs in biomedicine},
  year={2016},
  volume={127},
  pages={248-57}
}
BACKGROUND AND OBJECTIVE The automatic classification of breast imaging lesions is currently an unsolved problem. This paper describes an innovative representation learning framework for breast cancer diagnosis in mammography that integrates deep learning techniques to automatically learn discriminative features avoiding the design of specific hand-crafted image-based feature detectors. METHODS A new biopsy proven benchmarking dataset was built from 344 breast cancer patients' cases… CONTINUE READING

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