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Computer-aided detection of abnormality in mammography using deep object detectors

From National Research Council Canada

DOIResolve DOI: https://doi.org/10.1016/B978-0-12-819740-0.00001-2
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Affiliation
  1. National Research Council of Canada. Digital Technologies
FormatText, Book Chapter
Subjectmammography; deep learning; convolutional neural networks; transfer learning
Abstract
Computer-aided detection of medical abnormalities is of great importance for both doctors and patients. In this chapter, we use mammogram screening as an example and introduce two state-of-the-art deep neural networks for the detection of mass tissues. More specifically, we compare two-stage and one-stage object detectors using deep convolutional neural networks. With the limited number of training data, we use transfer learning to fine-tune the general object detectors on a publicly available mammogram dataset. Experimental results indicate that the deep learning-based approaches have great potential in mammogram screening.
Publication date
PublisherAcademic Press
Elsevier Inc.
In
  • State of the Art in Neural Networks and their Applications 1 (23 July 2021): 1–18.
LanguageEnglish
Peer reviewedYes
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Record identifierc2aa6997-c50c-4b73-ac26-e2e7d4a4c830
Record created2021-08-11
Record modified2021-08-11
Date modified: