GeoMedNet

# CESEN-GPARS: Interpretable Deep Learning for Medical Image Classification ## 🧠 Overview **CESEN-GPARS** is an interpretable deep learning framework for medical image classification. It is designed to extract non-linear features and preserve structural consistency across complex high-dimensional data. This framework introduces two key components: - **CESEN**: Curvature-Enhanced Signal Encoding Network- **GPARS**: Geometric Profile-Aligned Regularization Strategy Together, they provide curvature-aware representation and geometry-aligned optimization for robust and explainable classification in medical imaging. ## 🔍 Key Features - 💡 **Curvature-guided Feature Extraction**: Enhances local geometry using directional curvatures and angular modulation.- 🧩 **Geometric Profile Alignment**: Encourages global structural consistency via optimization-based surface fitting.- 🧠 **Interpretability-Preserving**: Maintains semantic structure and enhances visualization potential for medical diagnosis.- 📈 **Superior Generalization**: Outperforms state-of-the-art on ImageNet, Caltech-256, Oxford Flowers, and DTD datasets. ## 🧱 Architecture The framework consists of:- A backbone encoder with curvature-enhanced modules (CESEN)- A geometry-aware loss function (GPARS)- Classification head with semantic alignment 📌 *For details, refer to Figures and diagrams on pages 5–8 of the paper.* ## 📦 Project Structure