About the Quercus group

The Quercus group is dedicated to research in areas such as software engineering, model-driven engineering, data science, machine learning, quantum computing, computing continuum, and service-oriented computing. Its members are professors and researchers at the University of Extremadura who collaborate on R&D&i projects to drive technological innovation.

Quercus group news

International Summer School on LLM-based Agents for Software Engineering

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.

We open the (Metrika)Box

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

Everything everywhere… with just one prompt

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

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Quercus group projects

ADAPIMMA

SAM for medical image segmentation

The source code for this project is located in this repository.

MusicGenia

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

Quercus group publications

MetrikaBox: An open framework for experimenting with audio classification

This paper presents MetrikaBox, a general-purpose, open-source, and extensible audio classification package designed to facilitate the …

AI-Based System for Assistance in Minimally Invasive Renal Procedures Using Mixed Reality. First Steps

The main objective of this study is the implementation and configuration of an assistance system for minimally invasive renal …

Prompt Once, Segment Everything: Leveraging SAM 2 Potential for Infinite Medical Image Segmentation with a Single Prompt

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.

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