  Modern methods often formulate the counting of cells from microscopic images
as a regression problem and more or less rely on expensive, manually annotated
training images (e.g., dot annotations indicating the centroids of cells or
segmentation masks identifying the contours of cells). This work proposes a
supervised learning framework based on classification-oriented convolutional
neural networks (CNNs) to count cells from greyscale microscopic images without
using annotated training images. In this framework, we formulate the cell
counting task as an image classification problem, where the cell counts are
taken as class labels. This formulation has its limitation when some cell
counts in the test stage do not appear in the training data. Moreover, the
ordinal relation among cell counts is not utilized. To deal with these
limitations, we propose a simple but effective data augmentation (DA) method to
synthesize images for the unseen cell counts. We also introduce an ensemble
method, which can not only moderate the influence of unseen cell counts but
also utilize the ordinal information to improve the prediction accuracy. This
framework outperforms many modern cell counting methods and won the data
analysis competition (Case Study 1: Counting Cells From Microscopic Images
https://ssc.ca/en/case-study/case-study-1-counting-cells-microscopic-images) of
the 47th Annual Meeting of the Statistical Society of Canada (SSC). Our code is
available at https://github.com/anno2020/CellCount_TinyBBBC005.
