Abstract

Abstract

Abstract
The rapid growth of data traffic and evolving service demands are driving a shift from traditional network architectures to advanced solutions. While 5G networks provide reduced latency and higher availability, they still face limitations due to reliance on integrated hardware, leading to configuration and interoperability challenges. The emerging Open Radio Access Network (O-RAN) paradigm addresses these issues by enabling remote configuration and management of virtualized components through open interfaces, promoting cost-effective, multi-vendor interoperability. Network slicing, a key 5G enabler, allows for tailored network configurations to meet heterogeneous performance requirements. The main contribution of this paper is a private Standalone 5G network based on O-RAN, featuring a dynamic Data Radio Bearer Management xApp (xDRBM) for real-time metric collection and traffic prioritization. xDRBM optimizes resource usage and ensures performance guarantees for specific applications. Validation was conducted in an emulated environment representative of real-world scenarios. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

Abstract

Abstract
The main challenges hindering the adoption of deep learning-based systems in clinical settings are the scarcity of annotated data and the lack of interpretability and trust in these systems. Concept Bottleneck Models (CBMs) offer inherent interpretability by constraining the final disease prediction on a set of human-understandable concepts. However, this inherent interpretability comes at the cost of greater annotation burden. Additionally, adding new concepts requires retraining the entire system. In this work, we introduce a novel two-step methodology that addresses both of these challenges. By simulating the two stages of a CBM, we utilize a pretrained Vision Language Model (VLM) to automatically predict clinical concepts, and an off-the-shelf Large Language Model (LLM) to generate disease diagnoses grounded on the predicted concepts. Furthermore, our approach supports test-time human intervention, enabling corrections to predicted concepts, which improves final diagnoses and enhances transparency in decision-making. We validate our approach on three skin lesion datasets, demonstrating that it outperforms traditional CBMs and state-of-the-art explainable methods, all without requiring any training and utilizing only a few annotated examples. The code is available at https://github.com/CristianoPatricio/2step-concept-based-skin-diagnosis.

Abstract
Cardiovascular diseases are one of the main causes of death in the world. The predominant form of cardiovascular disease is coronary artery disease. Coronary artery calcium scanning is a non-contrast computed tomography exam that is considered the most reliable predictor of coronary events. Deep learning models have been developed for the segmentation of coronary artery calcium but the results have limited interpretability due to the black-box nature of these models. This work proposes an image retrieval pipeline based on a supervised contrastive framework that is capable of enhancing this interpretability by providing similar visual examples of coronary calcifications. In the COCA dataset, it is shown that this retrieval presents a label precision of 0.944 ± 0.230 regarding artery labels of retrieved images, with moderate similarity in terms of calcification area and Agatston score. It is also shown that the retrieval can be used to correct a deep CAC segmentation model by passing predictions from a segmentation model through the retrieval system, improving robustness and explainability.Clinical relevance- This study enhances CAC segmentation through image retrieval, improving both explainability and artery-specific labeling. By providing clinicians with more interpretable and anatomically accurate results, our approach aims to increase confidence in AI-assisted diagnostics leading to better-informed clinical decision-making in coronary artery disease diagnosis.