Multi-marginal Contrastive Learning for Multilabel Subcellular Protein Localization

Protein subcellular localization(PSL) is an important task to study human cell functions and cancer pathogenesis. It has attracted great attention in the computer vision community. However, the huge size of immune histochemical (IHC) images, the disorganized location distribution in different tissue images and the limited training images are always the challenges for the PSL to learn a strong generalization model with deep learning. In this paper, we propose a deep protein subcellular localization method with multi-marginal contrastive learning to perceive the same PSLs in different tissue images and different PSLs within the same tissue image. In the proposed method, we learn the representation of an IHC image by fusing the global features from the downsampled images and local features from the selected patches with the activation map to tackle the oversize of an IHC image. Then a multi-marginal attention mechanism is proposed to generate contrastive pairs with different margins and improve the discriminative features of PSL patterns effectively. Finally, the ensemble prediction of each IHC image is obtained with different patches. The results on the benchmark datasets show that the proposed method achieves significant improvements for the PSL task.