Convolutional neural networks (CNNs) provide reliable segmentation results on biomedical images. However, they can only develop their full potential with a representative dataset. Unfortunately, a large dataset is hard to create in biomedical research, since labeling images is time consuming and requires expert knowledge. Active learning seeks to determine those images that will yield the best results, which effectively reduces labeling cost. We present an active learning method for the stepwise identification of images that should be labeled next and test this method on an axon segmentation dataset. We outperform a baseline and a state-of-the-art method.
Original languageEnglish
Number of pages2
Publication statusPublished - 16.02.2020
EventAUTOMED - Automation in Medical Engineering 2020 - Lübeck, Germany
Duration: 02.03.202003.03.2020


ConferenceAUTOMED - Automation in Medical Engineering 2020

Research Areas and Centers

  • Centers: Center for Artificial Intelligence Luebeck (ZKIL)
  • Research Area: Intelligent Systems

DFG Research Classification Scheme

  • 409-05 Interactive and Intelligent Systems, Image and Language Processing, Computer Graphics and Visualisation


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