Web: http://arxiv.org/abs/2203.10726

June 20, 2022, 1:13 a.m. | Di Liu, Yunhe Gao, Qilong Zhangli, Ligong Han, Xiaoxiao He, Zhaoyang Xia, Song Wen, Qi Chang, Zhennan Yan, Mu Zhou, Dimitris Metaxas

cs.CV updates on arXiv.org arxiv.org

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 …

arxiv fusion image medical segmentation transformers

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