Multi-disciplinary Trends in Artificial Intelligence

Table of Contents

1. Global Positional Encoding and Its Application in Medical Image Segmentation

   Minh-Quy Le, Thanh-Sach Le

   Abstract

   Transformer-based models have become increasingly popular for medical image segmentation. While they incorporate positional encoding to model spatial structure, this encoding only captures patch positions within a cropped 3D subvolume, not within the full anatomical scan. As a result, important global spatial context–such as organ location priors–is lost. This limitation is particularly harmful in medical scenarios where multiple organs may share similar intensity profiles but differ in anatomical position.

   In this work, we propose a lightweight Global Positional Encoding (GPE) module that injects absolute 3D spatial coordinates into transformer-based segmentation networks. GPE recovers lost anatomical information and enhances spatial awareness without significant overhead. We integrate GPE into four representative models–UNETR, Swin-UNETR, nnFormer, and UNETR++–and evaluate on the Synapse multi-organ CT dataset. Results show consistent performance gains across all models, with up to 1.66% improvement in Dice score and substantial reduction in HD95.

   These findings demonstrate that GPE effectively bridges the gap between local processing and global spatial reasoning, offering a simple yet powerful enhancement for medical segmentation networks.

2. Integrating Graph Convolutional Networks and Clustering for Intelligent Recommendation System

   Thanh-Tung Dang, Thanh-Van Le

   Abstract

   In the era of information explosion, personalized recommendation systems have become indispensable tools for filtering relevant content for users. However, their performance is limited by challenges including the cold-start problem, sparse data, and difficulty in capturing complex user-item relationships. This research proposes HybridGCN-Ext, a novel deep learning approach that combines Graph Convolutional Networks (GCNs) with knowledge graphs and clustering techniques to address these limitations. Unlike traditional methods relying solely on collaborative or content-based filtering, our model leverages three distinct information sources: (1) user-item interaction patterns through a simplified LightGCN architecture, (2) semantic relationships between items through Knowledge Graph Convolutional Networks (KGCN), and (3) cluster-based information through attention mechanisms to enhance item representations. Experimental results demonstrate significant improvements across recommendation quality metrics. Our findings contribute to the recommendation systems field by demonstrating how structural knowledge and clustering can be effectively combined with graph neural networks to generate more accurate, diverse, and interpretable recommendations.

3. Backmatter