Deep Adaptive Few Example Learning for Microscopy Image Cell Counting

Deep learning networks have demonstrated robust performance for cell counting in medical images. However, obtaining promising results requires large amount of annotated data for supervised training, which is labor-intensive. To address this problem, we propose a novel adaptive few example counting network that aims at localizing cells in microscopy images with only a few annotated examples. We inc...