  Combining information from multi-view images is crucial to improve the
performance and robustness of automated methods for disease diagnosis. However,
due to the non-alignment characteristics of multi-view images, building
correlation and data fusion across views largely remain an open problem. In
this study, we present TransFusion, a Transformer-based architecture to merge
divergent multi-view imaging information using convolutional layers and
powerful attention mechanisms. In particular, the Divergent Fusion Attention
(DiFA) module is proposed for rich cross-view context modeling and semantic
dependency mining, addressing the critical issue of capturing long-range
correlations between unaligned data from different image views. We further
propose the Multi-Scale Attention (MSA) to collect global correspondence of
multi-scale feature representations. We evaluate TransFusion on the
Multi-Disease, Multi-View \& Multi-Center Right Ventricular Segmentation in
Cardiac MRI (M\&Ms-2) challenge cohort. TransFusion demonstrates leading
performance against the state-of-the-art methods and opens up new perspectives
for multi-view imaging integration towards robust medical image segmentation.
