Classifying Mixed Patterns of Proteins in High-Throughput Microscopy Images Using Deep Neural Networks

Abstract

Proteins contribute significantly in most body functions within cells, and are essential to the physiological activities of every creature. Microscopy imaging, as a remarkable technique, is applied to observe and identify proteins in different kinds of cells, by which the analysis results are critical to the biomedical studies. However, as the development of high-throughput microscopy imaging, images of protein microscopy are generated in a faster pace ever, making it harder for experts to manually identify them. For better digging and understanding the information of the proteins in those huge amounts of images, it is urgent for methods to identify the mixed-patterned proteins within various cells automatically and accurately. Here in this paper, we design some novel and effective data preparation and preprocessing methods for high-throughput microscopy protein datasets. We propose ACP layer and “buffering” layers, using them to design customized architectures for some typical CNN classifiers with new inputs and head parts. The modifications let the models be more adaptive and accurate to our task. We train the models in more effective and efficient optimization strategies that we design, e.g., cycle learning with learning rate scheduling. Besides, greedy selection of thresholds and multi-sized models ensembling in the post-process stage are proposed to further improve the prediction accuracy. Our experimental results based on Human Protein Atlas datasets demonstrates that the proposed methods show an excellent performance in mixed-patterned protein classifications to date, even beyond the state-of-the-art architecture GapNet-PL by 0.02 to 0.03 in F1 score. The whole work reveals the usefulness of our methods for high-throughput microscopy protein images identification.