Recent advances in biomedical imaging have enabled the analysis of many different cell types. Automated cell detection is an important field of interest with a wide range of applications which permits statistical analysis of various cell parameters. However, learning based cell detectors tend to be specific to a particular imaging protocol and cell type. For a new dataset, a tedious re-training process is required. In this paper, we propose a novel method of training a cell detector that rapidly adapts to new datasets. First, the classiffication rules extracted from existing data are combined with the training samples of new data using transfer learning. Second, a global parameter is incorporated to refine the ranking of the classiffcation rules. Third, an active training method which selects the most valuable new training samples first is employed to improve the detection accuracy. We refer to the proposed method as the Globally Active Transfer Learning for Adaptive Boosting (GATLAB), a boosting-based transfer learning method that incorporates a global parameter and active training to minimize the number of training samples. We demonstrate that our method achieves the same performance as previous approaches with only 10% of the training effort.