A comparison of sampling strategies for histological image analysis

Histological image analysis methods often employ machine-learning classifiers in order to adapt to the huge variability of histological images. To train these classifiers, the user must select samples of the relevant image objects. In the field of active learning, there has been much research on sampling strategies that exploit the uncertainty of the current classification in order to guide the user to maximally informative samples. Although these approaches have the potential to reduce the training effort and increase the classification accuracy, they are very rarely employed in practice. In this paper, we investigate the practical value of uncertainty sampling in the context of histological image analysis. To obtain practically meaningful results, we have devised an evaluation algorithm that simulates the way a human interacts with a user interface. The results show that uncertainty sampling outperforms common random or error sampling strategies by achieving more accurate classification results with a lower number of training images.