Context-aware focal modulation for detection of inflammatory bowel diseases from MRE images

Detection of inflammation in MR enterography (MRE) images is essential for the diagnosis and treatment planning of inflammatory bowel diseases. However, variability in the size, location, and shape of inflammation presents challenges for automated detection systems. This often results in false positives due to the similar imaging characteristics shared between the inflammation and non-inflammation regions. In this study, we propose a novel method for detecting inflammation in MRE images by applying a context-aware Focal Modulation Network (FocalNet) to a Mask R-CNNbased approach. Unlike traditional self-attention mechanisms, the Focal Modulation Network prioritizes nearby regions and de-emphasizes distant areas. Our method integrates both visual features and distance-based contextual information, including the location of inflammation, via gating aggregation. Experimental results confirmed that the proposed method improved mAP and precision scores through false positive reduction.