Identifying Subcellular Locations from Images of Unknown Resolution

Our group has previously used machine learning techniques to develop computational systems to automatically analyse fluorescence microscope images and classify the location of the depicted protein. Based on this work, we developed a system, the Subcellular Location Image Finder (slif), which mines images from scientific journals for analysis. For some of the images in journals, the system is able to automatically compute the pixel resolution (the physical space represented by each pixel), by identifying a scale bar and processing the caption text. However, scale bars are not always included. For those images, the pixel resolution is unknown. Blindly feeding these images into the classification pipeline results in unacceptably low accuracy. We first describe methods that minimise the impact of this problem by training resolution-insensitive classifiers. We show that these techniques are of limited use as classifiers can only be made insensitive to resolutions which are similar to each other. We then approach the problem in a different way by trying to estimate the resolution automatically and processing the image based on this prediction. Testing on digitally down-sampled images shows that the combination of these two approaches gives classification results which are essentially as good as if the resolution had been known.