Gam-UNet for Semantic Segmentation

Rahma Aloui, Pranav Martini, Pandu Devarakota, Apurva Gala, Shishir K. Shah

Abstract: Accurate delineation of critical features, such as salt boundaries in seismic imaging and fine structures in medical images, is essential for effective analysis and decision-making. Traditional convolutional neural networks (CNNs) often face difficulties in handling complex data due to variations in scale, orientation, and noise. These limitations become particularly evident during the transition from proof-of-concept to real-world deployment, where models must perform consistently under diverse conditions. To address these challenges, we propose GAM-UNet, an advanced segmentation architecture that integrates learnable Gabor filters for enhanced edge detection, SCSE blocks for feature refinement, and multi-scale fusion within the U-Net framework. This approach improves feature extraction across varying scales and orientations. Trained using a combined Binary Cross-Entropy and Dice loss function, GAM-UNet demonstrates superior segmentation accuracy and continuity, outperforming existi