Abstract
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 language | English |
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Number of pages | 2 |
Publication status | Published - 16.02.2020 |
Event | AUTOMED - Automation in Medical Engineering 2020 - Lübeck, Germany Duration: 02.03.2020 → 03.03.2020 |
Conference
Conference | AUTOMED - Automation in Medical Engineering 2020 |
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Country/Territory | Germany |
City | Lübeck |
Period | 02.03.20 → 03.03.20 |
Research Areas and Centers
- Centers: Center for Artificial Intelligence Luebeck (ZKIL)
- Research Area: Intelligent Systems
DFG Research Classification Scheme
- 4.43-05 Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing