## 1 Introduction
A different approach relies on applying semantic segmentation and predicting the spatial density [38] or density maps [39]. However, these methods require pixel-level annotations of the images during training, which is typically hard to acquire due to high costs. Counting by regression, on the other hand, directly predicts the number of target instances in an image rather than detects the number of objects present. Various machine learning and deep-learning approaches are available for a regression task, such as Support Vector Regression (SVR) [40], Random Forest Regression (RF) [41], Nearest-neighbor regression (NNR) [42], and Ridge Regression (RR) [43]. Counting by segmentation can be considered to be a mix of the counting by detection and counting by regression method since cells are first segmented before they are regressed. For example, in [44] a Feature Pyramid Network was used to segment the images by first building a ground truth feature mask, before regressing on that feature mask. Although this method is less labor-intensive than methods that require annotating, it cannot overcome limitations such as cell clumping and overlap. More generally, all methods that require obtaining annotations for the entire training data are time-consuming and expensive, especially if we are only interested in the global count of cells, and not in additional information such as their shapes, sizes, or locations. When it comes to developing a cell counter, the problem could be considered to be a regression task, whereby we are interested in learning the relationship between image representations (which are usually image global features) and the number of cellspresent [45; 46]. The problem is, however, that many of these approaches treat the regression problem as a multi-class classification task. This means that the number of cells is considered to be a class ID, and images with the same number of cells belong to the same class. As a result, a model will view images with 2 cells or 500 cells just as far apart as images containing 50 and 51 cells. In other words, they are unable to properly learn ordering. Thus, it is important to model this task as a regression problem rather than a multi-class classification problem. Here, we propose use of a Convolutional Neural Network (CNN) that directly predicts the number of cells, i.e., it is a regression model. As a result, our approach does not require pixel annotations, which are typically very costly to obtain, which drastically improves its wide applicability. We will demonstrate that in comparison to approaches with hand-crafted features as input to a regressor, our method not only provides better performance, but it is also easy-to-use. Moreover, we present that the application of the transfer learning is essential to achieve a high performance when a small number of data are available. The contribution of this paper is three-fold:  
* A novel dataset containing images and the number of manually counted cells is presented. The images represent a human osteosarcoma cell line (U2 OS) and a human leukemia cell line (HL-60). The dataset was collected at the University of Amsterdam, and could be downloaded from: [[https://doi.org/10.5281/zenodo.4428844](https://doi.org/10.5281/zenodo.4428844)]([https://doi.org/10.5281/zenodo.4428844](https://doi.org/10.5281/zenodo.4428844)) (accessed on 26 May 2021). * The development of a pipeline for automatically counting cells using a convolutional neural network-based regressor. We indicate a specific architecture of a neural network that in combination with transfer learning allows the achievement of very promising performance. * We provide baseline results for the newly collected data. Moreover, we present that the proposed approach achieves a human-level performance.