Focal Modulation and Bidirectional Feature Fusion Network for Medical Image Segmentation

A research paper on arXiv proposes a Focal Modulation and Bidirectional Feature Fusion Network to improve medical image segmentation. Medical image segmentation is critical for clinical applications like disease diagnosis and treatment planning, providing precise morphological and spatial information. Convolutional neural networks (CNNs) are widely used but struggle with capturing global context and long-range dependencies due to their local receptive fields, affecting segmentation of complex borders and varied sizes. To address this, the network integrates transformer-based architecture with CNNs, leveraging self-attention for global context. The proposed method combines focal modulation and bidirectional feature fusion to enhance segmentation accuracy.