Deep Adaptive Few Example Learning for Microscopy Image Cell Counting

Meng Li, Kun Zhao, Can Peng, Brian C. Lovell

Abstract: 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 incorporate a few-shot learning phase to transfer information of features from general training images to novel medical images. We also present a training strategy that takes both novel images with their few instances as the input to predict the final results. Furthermore, we introduce an adaptation stage during test time which takes a query medical image as well as its corresponding few object instances to boost the network performance. As the task is firstly established, we further create three few-shot cell counting datasets by adding cell exemplars on top of the public medical images and set them as the baseline. Extensive experiments on the three microscopy datasets show that our framework outperforms the other baseline approaches.