Deep Reinforcement Learning for Active Breast Lesion Detection from DCE-MRI

  • Gabriel MaicasEmail author
  • Gustavo Carneiro
  • Andrew P. Bradley
  • Jacinto C. Nascimento
  • Ian Reid
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10435)


We present a novel methodology for the automated detection of breast lesions from dynamic contrast-enhanced magnetic resonance volumes (DCE-MRI). Our method, based on deep reinforcement learning, significantly reduces the inference time for lesion detection compared to an exhaustive search, while retaining state-of-art accuracy.

This speed-up is achieved via an attention mechanism that progressively focuses the search for a lesion (or lesions) on the appropriate region(s) of the input volume. The attention mechanism is implemented by training an artificial agent to learn a search policy, which is then exploited during inference. Specifically, we extend the deep Q-network approach, previously demonstrated on simpler problems such as anatomical landmark detection, in order to detect lesions that have a significant variation in shape, appearance, location and size. We demonstrate our results on a dataset containing 117 DCE-MRI volumes, validating run-time and accuracy of lesion detection.


Deep Q-learning Q-net Reinforcement learning Breast lesion detection Magnetic resonance imaging 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Gabriel Maicas
    • 1
    Email author
  • Gustavo Carneiro
    • 1
  • Andrew P. Bradley
    • 2
  • Jacinto C. Nascimento
    • 3
  • Ian Reid
    • 1
  1. 1.School of Computer Science, ACVTThe University of AdelaideAdelaideAustralia
  2. 2.School of ITEEThe University of QueenslandBrisbaneAustralia
  3. 3.Institute for Systems and RoboticsInstituto Superior TecnicoLisbonPortugal

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