Cell detection is an essential task for characterizing and studying tumor microenvironments (TME). Automatic cell detection in histopathology is challenging due to the diversity of cell shape, size, morphology, as well as stain variations between laboratories. Though deep learning has become the choice method to tackle this task, typically training requires a large number of annotations, which can be laborious and time consuming. While recent developments to tackle this annotation problem have seen success, typically these pipelines add complexity to training and may not be easy to implement. In this paper we demonstrate that using several public datasets we can train a competitive cell detection network for Barrett's Esophagus (BE) premalignant tissue samples using conventional supervised training methods. To adapt the network to clinical BE tissue sections, pseudolabels were generated to retrain the network. The results indicate that public cell detection datasets can be used to train networks that generalize well to pre-cancer tissue samples without requiring any manual annotations, which can accelerate digital pathology research for early detection.