DDSM digitized film mammograms can be transferred to INbreast FFDM images using only a subset of the INbreast data for fine-tuning and without further reliance on the availability of lesion annotations. These findings show that automatic deep learning methods can be readily trained to attain high accuracy on heterogeneous mammography platforms, and hold tremendous promise for improving clinical tools to reduce false positive and false negative screening mammography results. Code and model available at: <https://github.com/lishen/end2end-all-conv>'
author:
- |
    Li Shen\
    Department of Neuroscience\
    Icahn School of Medicine at Mount Sinai (ISMMS)\
    New York, 10029, USA\
    `li.shen@mssm.edu`\
    Laurie R. Margolies\
    Department of Diagnostic, Molecular, and Interventional Radiology\
    ISMMS\
    Joseph H. Rothstein\
    Department of Genetics and Genomic Sciences\
    Department of Population Health Science and Policy\
    ISMMS\
    Eugene Fluder\
    Department of Scientific Computing\
    ISMMS\
    Russell B. McBride\
    Department of Pathology\
    ISMMS\
    Weiva Sieh\
    Department of Population Health Science and Policy\
    Department of Genetics and Genomic Sciences\
    ISMMS
bibliography:
- '/Users/lishen/Dropbox/3MyWorks/MyLibrary.bib'
title: Deep Learning to Improve Breast Cancer Early Detection on Screening Mammography
---

Introduction
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The rapid advancement of machine learning and especially deep learning continues to fuel the medical imaging community’s interest in applying these techniques to improve the accuracy of cancer 