Report and highlights from the 1st International Summer School on LLM-based Agents for Software Engineering (LLMA4SE), held from September 1-3, 2025 in Cáceres, Spain.

Join us learning what is the magic juice that moves MetrikaMedia, the leading SaaS for multimedia content measurement.

We continue our research in low-cost optimization of foundation models, specifically applied to medical image segmentation.

Copyright Analysis in Multimedia Pieces Broadcast in Audiovisual Media using Machine Learning

Cloud-based Platform for On-demand Music Generation by Artificial Intelligence

This paper presents MetrikaBox, a general-purpose, open-source, and extensible audio classification package designed to facilitate the development of Deep Learning (DL) models for a wide range of audio processing tasks. The software manages all necessary preprocessing steps to build classification models capable of distinguishing between user-defined classes using advanced Artificial Intelligence (AI) techniques. MetrikaBox is well suited for tasks such as musical genre classification, voice-versus-music discrimination, and other audio classification or segmentation applications. Users can either employ the package as provided or extend it by integrating their own datasets, classification models, data loading systems, augmentation techniques, and more. The package has been tested in both commercial and academic settings, where it has produced models for industrial audio processing and served as a platform for proof-of-concept applications. Comprehensive documentation and practical examples included in the repository support users in integrating the system into their audio analysis projects. MetrikaBox is openly available and provides a user interface for convenient testing.

The main objective of this study is the implementation and configuration of an assistance system for minimally invasive renal surgeries, which incorporates an Artificial Intelligence (AI)-based automatic renal anatomy segmentation module using computed tomography (CT) imaging studies, as well as its integration into an immersive and interactive mixed reality (MR) system. The purpose is to enrich surgical planning, ensuring greater precision and safety to improve patient outcomes. The image dataset used in this study comes from the KITS23 challenge, from which 20 CT studies were randomly selected from the 489 available with ground truth annotations. The interactive MR interface was developed using Unity in combination with the Microsoft HoloLens v2 device. For medical image segmentation, the Vista3D AI model was employed due to its versatility and high performance. All studies were successfully segmented, showing a Dice score distribution with a high concentration of values above 0.8 for renal anatomy segmentation, indicating robust and consistent performance. However, in the case of cyst segmentation, the Dice score distribution revealed a significant proportion of low values, reflecting the complexity of this type of anatomical structure. Additionally, an application was developed for MR visualization of 3D renal anatomical models to facilitate surgical planning. This application allows clinicians to more accurately identify renal anatomy, complementing and improving traditional planning methods. The development of this assistance system lays the foundation for greater precision, error reduction and better surgical outcomes, contributing to safer and more efficient procedures.

This article presents a novel approach that leverages SAM 2 video segmentation capabilities to reduce the number of prompts needed to segment a complete volume of medical images. The study first compares the performance of SAM and SAM 2 in medical image segmentation. Then, our novel approach is presented. The results show that SAM 2 achieves an average improvement of 1.76% in Jaccard Index and 1.49% in Dice Score compared to SAM.