Learning to Detect Monoclonal Protein in Electrophoresis Images

Monoclonal protein (M-protein) detection with electrophoresis is of vital importance for the diagnosis of lympho-proliferative processes and monoclonal gammopathies (MGs). Although identifying M-proteins are key for the diagnosis and monitoring of these disorders, it requires specialized knowledge and is time consuming and labor intensive. Despite existing powerful machine learning methods, it often requires to obtain large number of labeled data for training, which is difficult to obtain. Besides, electrophoresis image quality could vary dramatically, affecting the proper identification of M-protein. To address these challenges, we propose to represent electrophoresis images using Gaussian Mixture Model (GMM) and leverage peak detection method to identify visual features for M-protein detection. Utilizing random forest classifier, our method can work with a small amount of labeled data to train the model and is not sensitive to samples of varying quality. Furthermore, with extracted image features, it is possible for specially trained technologists and pathologists to understand and check the decision process of the learned model. Extensive experiments indicate our proposed method achieves satisfactory results on test data, demonstrating the effectiveness and robustness of the proposed model for M-protein detection.