Large image modality labeling initiative using semi-supervised and optimized clustering

Medical image modality detection is a key step for indexing images from biomedical articles. Traditionally, complex supervised classification methods have been used for this. However, they rely on proportionally sized labeled training samples. With the increase in availability of image data it has become increasingly challenging to obtain reasonably accurate manual labels to train classifiers. Toward meeting this shortcoming, we propose a semi-automatic labeling strategy that reduces the human annotator effort. Each image is projected into several feature spaces, and each entry in these spaces is clustered in an unsupervised manner. The cluster centers for each feature representation are then labeled by a human annotator, and the labels propagated through each cluster. To find the optimal cluster numbers for each feature space, a so-called “jump” method is used. The final label of an image is decided by a voting scheme that summarizes the different opinions on the same image provided by the different feature representations. The proposed method is evaluated on ImageCLEFmed2012 data set containing approximately 300,000 images, and showed that annotating \(<\)1 % of the data is sufficient to label correctly 49.95 % of the images. The method spared approximately 700 h of human annotation labor and associated costs.