Cross-attention guided loss-based deep dual-branch fusion network for liver tumor classification

Rui Wang, Xiaoshuang Shi, Shuting Pang, Yidi Chen, Xiaofeng Zhu, Wentao Wang, Jiabin Cai, Danjun Song, Kang Li

Abstract: Highlights•We introduce a dual-branch network with cross-attention for liver tumor-related classification.•We design a cross-attention module between two branches for interpreting lesion-relevant regions.•We propose a novel loss function to reduce attention mechanism inconsistency.