Classification of Subcellular Protein Patterns in Human Cells with Transfer Learning

Zongyao Li, Ren Togo, Takahiro Ogawa, Miki Haseyama

Abstract: In this paper, we present a deep learning method for classifying subcellular protein patterns in human cells. Our method is mainly based on transfer learning and utilizes a newly proposed loss function named focal loss to deal with the problem of severe class imbalance existing in the task. The performance of our method is evaluated by a MacroF1 score of total 28 classes, and the final MacroF1 score of our method is 0.706.