Rapidly Adaptive Cell Detection Using Transfer Learning with a Global Parameter
Recent advances in biomedical imaging have enabled the analysis of many different cell types. 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 present a novel method of training a cell detector on new datasets with minimal effort. First, we combine the classification rules extracted from existing data with the training samples of new data using transfer learning. Second, a global parameter is incorporated to refine the ranking of the classification rules. We demonstrate that our method achieves the same performance as previous approaches with only 10% of the training effort.
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