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Researcher profile

D. Gil

D. Gil contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Machine LearningArtificial IntelligenceComputer Visioneess.IVhep-exnucl-exSocial and Information Networks

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Published work

5 published item(s)

preprint2026arXiv

How Sensitive Are Radiomic AI Models to Acquisition Parameters?

A main barrier for the deployment of AI radiomic systems in clinical routine is their drop in performance under heterogeneous multicentre acquisition protocols. This work presents a performance-oriented framework for quantifying scan parameter sensitivity of radiomic AI models, while identifying clinically significant parameter regions associated with improved cross-dataset robustness. We formulate a mixed-effects framework for quantifying the influence that clinically relevant acquisition parameters have on models performance, while accounting for subject-level random effects. We have applied our framework to lung cancer diagnosis in CT scans using two independent multicentre datasets (a public database and own-collected data) and several SoA architectures. To evaluate across-database reproducibility, CT parameters have been adjusted using the data collected and tested on the public set. The optimal configuration selected is the current of the X-ray tube >= 200 mA, spiral pitch <= 1.5, slice thickness <= 1.25 mm, which balances diagnostic quality with low radiation dose. These configuration push metrics from 0.79+-0.04 sensitivity, 0.47+-0.10 specificity in low quality scans to 0.90+-0.10 sensitivity, 0.79 +- 0.13 specificity in high quality ones.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A flexible outlier detector based on a topology given by graph communities

Outlier, or anomaly, detection is essential for optimal performance of machine learning methods and statistical predictive models. It is not just a technical step in a data cleaning process but a key topic in many fields such as fraudulent document detection, in medical applications and assisted diagnosis systems or detecting security threats. In contrast to population-based methods, neighborhood based local approaches are simple flexible methods that have the potential to perform well in small sample size unbalanced problems. However, a main concern of local approaches is the impact that the computation of each sample neighborhood has on the method performance. Most approaches use a distance in the feature space to define a single neighborhood that requires careful selection of several parameters. This work presents a local approach based on a local measure of the heterogeneity of sample labels in the feature space considered as a topological manifold. Topology is computed using the communities of a weighted graph codifying mutual nearest neighbors in the feature space. This way, we provide with a set of multiple neighborhoods able to describe the structure of complex spaces without parameter fine tuning. The extensive experiments on real-world data sets show that our approach overall outperforms, both, local and global strategies in multi and single view settings.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Early Screening of SARS-CoV-2 by Intelligent Analysis of X-Ray Images

Future SARS-CoV-2 virus outbreak COVID-XX might possibly occur during the next years. However the pathology in humans is so recent that many clinical aspects, like early detection of complications, side effects after recovery or early screening, are currently unknown. In spite of the number of cases of COVID-19, its rapid spread putting many sanitary systems in the edge of collapse has hindered proper collection and analysis of the data related to COVID-19 clinical aspects. We describe an interdisciplinary initiative that integrates clinical research, with image diagnostics and the use of new technologies such as artificial intelligence and radiomics with the aim of clarifying some of SARS-CoV-2 open questions. The whole initiative addresses 3 main points: 1) collection of standardize data including images, clinical data and analytics; 2) COVID-19 screening for its early diagnosis at primary care centers; 3) define radiomic signatures of COVID-19 evolution and associated pathologies for the early treatment of complications. In particular, in this paper we present a general overview of the project, the experimental design and first results of X-ray COVID-19 detection using a classic approach based on HoG and feature selection. Our experiments include a comparison to some recent methods for COVID-19 screening in X-Ray and an exploratory analysis of the feasibility of X-Ray COVID-19 screening. Results show that classic approaches can outperform deep-learning methods in this experimental setting, indicate the feasibility of early COVID-19 screening and that non-COVID infiltration is the group of patients most similar to COVID-19 in terms of radiological description of X-ray. Therefore, an efficient COVID-19 screening should be complemented with other clinical data to better discriminate these cases.

0 citations0 reviewsNo code linkedOpen access
preprint2009arXiv

Invariant mass distributions for the pp to p p eta reaction at Q=10 MeV

Proton-proton and proton-eta invariant mass distributions and the total cross section for the pp to pp eta reaction have been determined near the threshold at an excess energy of Q=10 MeV. The experiment has been conducted using the COSY-11 detector setup and the cooler synchrotron COSY. The determined invariant mass spectra reveal significant enhancements in the region of low proton-proton relative momenta, similarly as observed previously at higher excess energies of Q=15.5 MeV and Q= 40MeV.

0 citations0 reviewsNo code linkedOpen access
preprint2006arXiv

Eta and eta&#39; mesons production at COSY-11

The low emittance and small momentum spread of the proton and deuteron beams of the Cooler Synchrotron COSY combined with the high mass resolution of the COSY-11 detection system permit to study the creation of mesons in the nucleon-nucleon interaction down to the fraction of MeV with respect to the kinematical threshold. At such small excess energies, the ejectiles possess low relative momenta and are predominantly produced with the relative angular momentum equal to zero. Taking advantage of these conditions we have performed investigations aiming to determine the mechanism of the production of eta and eta&#39; mesons in the collision of hadrons as well as the hadronic interaction of these mesons with nucleons and nuclei. In this proceedings we address the ongoing studies of the spin and isospin dependence for the production of the eta and eta&#39; mesons in free and quasi-free nucleon-nucleon collisions. New results on the spin observables for the vec(p)p --> pp eta reaction, combined with the previously determined total cross section isospin dependence, reveal a statistically significant indication that the excitation of the nucleon to the S11(1535) resonance, the process which intermediates the production of the eta meson in the nucleon-nucleon interactions, is predominantly due to the exchange of the pi meson between the colliding nucleons.

0 citations0 reviewsNo code linkedOpen access