Explanipedia

On the robustness of [18F]-FDG-PET radiomic features to variations in image acquisition and reconstruction settings: A phantom study Open

Syafiq Ramlee, Maria Delgado-Ortet, L. Escudero, Luigi Aloj, Roido Manavaki · 2025

Background Greater access to clinically meaningful data from [ 18 F]-FDG-PET images could be made possible through radiomics. However, the vulnerability of radiomic measurements to changes in image acquisition and reconstruction settings h…

A European Multi-Center Breast Cancer MRI Dataset Open

Gustav Müller‐Franzes, L. Escudero, Nicholas Roy Payne, Alexandra Athanasiou, Michael Kalogeropoulos , et al. · 2025

Detecting breast cancer early is of the utmost importance to effectively treat the millions of women afflicted by breast cancer worldwide every year. Although mammography is the primary imaging modality for screening breast cancer, there i…

A Pipeline for Automated Quality Control of Chest Radiographs Open

Ian Selby, E. González-Solares, Anna Breger, Michael Roberts, L. Escudero , et al. · 2025

This article presents a suite of quality control tools for chest radiographs based on traditional and artificial intelligence methods, developed and tested with data from 39 centers in seven countries.

Swarm learning with weak supervision enables automatic breast cancer detection in magnetic resonance imaging Open

Oliver Lester Saldanha, Jiefu Zhu, Gustav Müller‐Franzes, Zunamys I. Carrero, Nicholas Roy Payne , et al. · 2025

Background Over the next 5 years, new breast cancer screening guidelines recommending magnetic resonance imaging (MRI) for certain patients will significantly increase the volume of imaging data to be analyzed. While this increase poses ch…

SpeedyAnnotate: An Intuitive and Open-Source Tool for Efficient Image Annotation and Quality Comparison Open

Ian Selby, Anna Breger, Michael S. Roberts, L. Escudero, Judith Babar , et al. · 2025

Improving the generalisation of radiographic AI using automated data curation to mitigate shortcut learning Open

Ian Selby, E. González-Solares, Anna Breger, Michael Roberts, L. Escudero , et al. · 2025

AUGMENT: a framework for robust assessment of the clinical utility of segmentation algorithms Open

Cathal McCague, Thomas Buddenkotte, L. Escudero, David Hulse, Roxana Pintican , et al. · 2024

Background Evaluating AI-based segmentation models primarily relies on quantitative metrics, but it remains unclear if this approach leads to practical, clinically applicable tools. Purpose To create a systematic framework for evaluating t…

Mitigating the impact of image processing variations on tumour [18F]-FDG-PET radiomic feature robustness Open

Syafiq Ramlee, Roido Manavaki, Luigi Aloj, L. Escudero · 2024

Radiomics analysis of [ 18 F]-fluorodeoxyglucose ([ 18 F]-FDG) PET images could be leveraged for personalised cancer medicine. However, the inherent sensitivity of radiomic features to intensity discretisation and voxel interpolation compl…

Deep learning-based segmentation of multisite disease in ovarian cancer Open

Thomas Buddenkotte, Leonardo Rundo, Ramona Woitek, L. Escudero, Lucian Beer , et al. · 2023

Purpose To determine if pelvic/ovarian and omental lesions of ovarian cancer can be reliably segmented on computed tomography (CT) using fully automated deep learning-based methods. Methods A deep learning model for the two most common dis…

Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer Open

Mireia Crispin‐Ortuzar, Ramona Woitek, Marika Reinius, Elizabeth Moore, Lucian Beer , et al. · 2023

Integrating Artificial Intelligence Tools in the Clinical Research Setting: The Ovarian Cancer Use Case Open

L. Escudero, Thomas Buddenkotte, Mohammad Al Sa’d, Cathal McCague, James A. d’Arcy , et al. · 2023

Artificial intelligence (AI) methods applied to healthcare problems have shown enormous potential to alleviate the burden of health services worldwide and to improve the accuracy and reproducibility of predictions. In particular, developme…

Public and patient involvement: a survey on knowledge, experience and opinions among researchers within a precision oncology European project Open

Paola Mosconi, Cinzia Colombo, Pasquale Paletta, Laura Gangeri, Chiara Pellegrini , et al. · 2023

A pipeline to further enhance quality, integrity and reusability of the NCCID clinical data Open

Anna Breger, Ian Selby, Michael Roberts, Judith Babar, Effrossyni Gkrania‐Klotsas , et al. · 2023

The National COVID-19 Chest Imaging Database (NCCID) is a centralized UK database of thoracic imaging and corresponding clinical data. It is made available by the National Health Service Artificial Intelligence (NHS AI) Lab to support the …

Position statement on clinical evaluation of imaging AI Open

Cathal McCague, Katherine Mackay, Ceilidh Welsh, Alex Constantinou, R. Jena , et al. · 2023

Governments and medical associations across the world, including the US Food and Drug Administration, the UK Medicines and Healthcare products Regulatory Agency, the Royal College of Radiologists, and the European Society of Radiology, bel…

Calibrating ensembles for scalable uncertainty quantification in deep learning-based medical image segmentation Open

Thomas Buddenkotte, L. Escudero, Mireia Crispin‐Ortuzar, Ramona Woitek, Cathal McCague , et al. · 2023

Uncertainty quantification in automated image analysis is highly desired in many applications. Typically, machine learning models in classification or segmentation are only developed to provide binary answers; however, quantifying the unce…

Deep learning-based Segmentation of Multi-site Disease in Ovarian Cancer Open

Thomas Buddenkotte, Leonardo Rundo, Ramona Woitek, L. Escudero, Lucian Beer , et al. · 2023

Purpose To determine if pelvic/ovarian and omental lesions of ovarian cancer can be reliably segmented on computed tomography (CT) using fully automated deep learning-based methods. Materials and Methods A deep learning model for the two m…

Artificial intelligence for early detection of renal cancer in computed tomography: A review Open

William C. McGough, L. Escudero, Cathal McCague, Grant D. Stewart, Carola‐Bibiane Schönlieb , et al. · 2022

Renal cancer is responsible for over 100,000 yearly deaths and is principally discovered in computed tomography (CT) scans of the abdomen. CT screening would likely increase the rate of early renal cancer detection, and improve general sur…

Author Correction: Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence Open

Xiang Bai, Hanchen Wang, Liya Ma, Yongchao Xu, Jiefeng Gan , et al. · 2022

[This corrects the article DOI: 10.1038/s42256-021-00421-z.].

Integrating the OHIF Viewer into XNAT: Achievements, Challenges and Prospects for Quantitative Imaging Studies Open

Simon Doran, Mohammad Al Sa’d, James Petts, James A. d’Arcy, Kate Alpert , et al. · 2022

Purpose: XNAT is an informatics software platform to support imaging research, particularly in the context of large, multicentre studies of the type that are essential to validate quantitative imaging biomarkers. XNAT provides import, arch…

Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence Open

Xiang Bai, Hanchen Wang, Liya Ma, Yongchao Xu, Jiefeng Gan , et al. · 2021

Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer Open

Mireia Crispin‐Ortuzar, Ramona Woitek, Elizabeth Moore, Marika Reinius, Lucian Beer , et al. · 2021

High grade serous ovarian cancer (HGSOC) is a highly heterogeneous disease that often presents at an advanced, metastatic state. The multi-scale complexity of HGSOC is a major obstacle to measuring response to neoadjuvant chemotherapy (NAC…

Ultrasound-guided targeted biopsies of CT-based radiomic tumour habitats: technical development and initial experience in metastatic ovarian cancer Open

Lucian Beer, Paula Martín-González, Maria Delgado-Ortet, Marika Reinius, Leonardo Rundo , et al. · 2020

Search for heavy neutral leptons decaying into muon-pion pairs in the MicroBooNE detector Open

P. Abratenko, M. Alrashed, R. An, J. Anthony, J. Asaadi , et al. · 2020

We present upper limits on the production of heavy neutral leptons (HNLs) decaying to $\\mu \\pi$ pairs using data collected with the MicroBooNE liquid-argon time projection chamber (TPC) operating at Fermilab. This search is the first of …

Reconstruction and measurement of 𝒪(100) MeV energy electromagnetic activity from π0 arrow γγ decays in the MicroBooNE LArTPC Open

C. Adams, M. Alrashed, R. An, J. Anthony, J. Asaadi , et al. · 2020

We present results on the reconstruction of electromagnetic (EM) activity from photons produced in charged current νµ interactions with final state π 0 s. We employ a fully-automated reconstruction chain capable of identifying EM showers o…

First Measurement of Inclusive Muon Neutrino Charged Current Differential Cross Sections on Argon at with the MicroBooNE Detector Open

P. Abratenko, C. Adams, M. Alrashed, R. An, J. Anthony , et al. · 2019

We report the first measurement of the double-differential and total muon neutrino charged current inclusive cross sections on argon at a mean neutrino energy of 0.8 GeV. Data were collected using the MicroBooNE liquid argon time projectio…

First measurement of charged-current production on argon with the MicroBooNE detector Open

C. Adams, M. Alrashed, R. An, J. Anthony, J. Asaadi , et al. · 2019

We report the first measurement of the flux-integrated cross section of νμ charged-current single π0 production on argon. This measurement is performed with the MicroBooNE detector, an 85 ton active mass liquid argon time projection chambe…

Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber Open

C. Adams, M. Alrashed, R. An, J. Anthony, J. Asaadi , et al. · 2019

We have developed a convolutional neural network that can make a pixel-level prediction of objects in image data recorded by a liquid argon time projection chamber (LArTPC) for the first time. We describe the network design, training techn…

Comparison of νμ-Ar multiplicity distributions observed by MicroBooNE to GENIE model predictions : MicroBooNE Collaboration Open

C. Adams, R. An, J. Anthony, J. Asaadi, M. Auger , et al. · 2019

We measure a large set of observables in inclusive charged current muon neutrino scattering on argon with the MicroBooNE liquid argon time projection chamber operating at Fermilab. We evaluate three neutrino interaction models based on the…

Ionization electron signal processing in single phase LArTPCs. Part I. Algorithm Description and quantitative evaluation with MicroBooNE simulation Open

C. Adams, R. An, J. Anthony, J. Asaadi, M. Auger , et al. · 2018

© 2018 IOP Publishing Ltd and Sissa Medialab. We describe the concept and procedure of drifted-charge extraction developed in the MicroBooNE experiment, a single-phase liquid argon time projection chamber (LArTPC). This technique converts …

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