A Multi-task Method for Immunofixation Electrophoresis Image Classification

In the field of plasma cell disorders diagnosis, the detection of abnormal monoclonal (M) proteins through Immunofixation Electrophoresis (IFE) is a widely accepted practice. However, the classification of IFE images into nine distinct categories is a complex task due to the significant class imbalance problem. To address this challenge, a two-sub-task classification approach is proposed, which divides the classification task into the determination of severe and mild cases, followed by their combination to produce the final result. This strategy is based on the expert understanding that the nine classes are different combinations of severe and mild cases. Additionally, the examination of the dense band co-location on the electrophoresis lane and other lanes is crucial in the expert evaluation of the image class. To incorporate this expert knowledge into the model training, inner-task and inter-task regularization is introduced. The effectiveness of the proposed method is demonstrated through experiments conducted on approximately 15,000 IFE images, resulting in interpretable visualization outcomes that are in alignment with expert expectations. Codes are available at https://github.com/shiy19/IFE-classification.