ECINFusion: A Novel Explicit Channel-Wise Interaction Network for Unified Multi-Modal Medical Image Fusion

Multi-modal medical image fusion enhance the representation, aggregation and comprehension of functional and structural information, improving accuracy and efficiency for subsequent analysis. However, lacking explicit cross channel modeling and interaction among modalities results in the loss of details and artifacts. To this end, we propose a novel <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</u>xplicit <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u>hannel-wise <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</u>nteraction <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</u>etwork for unified multi-modal medical image <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fusion</u>, namely ECINFusion. ECINFusion encompasses two components: multi-scale adaptive feature modeling (MAFM) and explicit channel-wise interaction mechanism (ECIM). MAFM leverages adaptive parallel convolution and transformer in multi-scale manner to achieve the global context-aware feature representation. ECIM utilizes the designed multi-head channel-attention mechanism for explicit modeling in channel dimension to accomplish the cross-modal interaction. Besides, we introduce a novel adaptive L-Norm loss, preserving fine-grained details. Experiments demonstrate ECINFusion outperforms state-of-the-art approaches in various medical fusion sub-tasks on different metrics. Furthermore, extended experiments reveal the robust generalization of the proposed in different fusion tasks. In breif, the proposed explicit channel-wise interaction mechanism provides new insight for multi-modal interaction.