Self-Supervised Fusion for Multi-Modal Medical Images via Contrastive Auto-Encoding and Convolutional Information Exchange Ying Zhang and Rencan Nie Yunnan University, CHINA Jinde Cao Southeast University, CHINA Chaozhen Ma Yunnan University, CHINA Digital Object Identifier 10.1109/MCI.2022.3223487 Date ofcurrent version: 13January 2023 Abstract-This paper proposes a self-supervised framework based on a contrastive auto-encoding and convolutional information exchange for multi-modal medical fusion tasks. It is well known that multi-modal medical images have the same and unique features, and information redundancy is easily led when source image features are extracted in pairs. Inspired by contrastive learning, this article constructs positive and negative results pairs and proposes a novel contrastive loss in Corresponding author: Rencan Nie (e-mail: rcnie@ynu.edu.cn). 68 IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | FEBRUARY 2023 1556-603X ß 2023 IEEE IMAGE LICENSED BY INGRAM PUBLISHINGhttps://orcid.org/0000-0003-0568-1231