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Confidence Estimation in Stem Cell Classification



Peter Bajcsy, Jana Kosecka, Zahra Rajabi


We study the problem of supervised classification of stem cell colonies and confidence estimation of the attained classification labels. The problem is investigated in the application context of heterogeneity labels of stem cell colonies observed by using fluorescent microscopy imaging. Given the colony images and their characterization using numerous image statistics, we report the classification results using adaptive k-Nearest Neighbor (NN) algorithm. This algorithm minimizes typical k-NN classification bias by giving more weight to more informative features in predicting class posterior probabilities. We then estimate the confidence of each prediction for unlabeled data using p-value and strangeness metrics. We show that such an introspective ability can gradually increase the learned model accuracy, quantify false positives, and guide the resource-limited manual colony annotation process to provide training labels for the less confident unlabeled samples.
Proceedings Title
BioImage Informatics Conference 2015
Conference Dates
October 14-16, 2015
Conference Location
Gaithersburg, MD, US


classifiers, uncertainty, bias


Bajcsy, P. , Kosecka, J. and Rajabi, Z. (2015), Confidence Estimation in Stem Cell Classification, BioImage Informatics Conference 2015, Gaithersburg, MD, US, [online], (Accessed April 17, 2024)
Created October 15, 2015, Updated April 6, 2022