publications

2026

1. Statistical Analysis of Coronal Hole Spatiotemporal Evolution, Kinematics, and Lifetimes over 1.5 Solar Cycles

   J Liu, C Shen, Y Chi, and 8 more authors

   Astrophysical Journal, 2026
2. Comparing Solar Structure Detection Methods in SDO/AIA Observations and the Application to Raw Uncalibrated Data

   P. Gonidakis, F. Carella, E. Dineva, and 9 more authors

   Journal of Geophysical Research: Machine Learning and Computation, 2026

   e2025JH001023 2025JH001023

   Abs DOI

   Abstract Recent advances in solar physics increasingly rely on automated identification of coronal structures using machine learning. Yet most studies emphasize scientific performance without evaluating feasibility for onboard deployment to prioritize downlink observations. This work investigates the automated identification of active regions and coronal holes by applying segmentation and detection techniques to Solar Dynamics Observatory (SDO) data. We compare three approaches: SCSS-Net, a deep learning model for semantic segmentation; YOLOv8n, a lightweight object detector; and a traditional pipeline based on basic computer vision operations (BCVO). Each method is assessed for its scientific accuracy and its suitability for deployment in future resource-limited missions. While no direct hardware benchmarking is performed in this study, we assess the feasibility of onboard implementation based on the associated number of trainable parameters, architecture and hardware requirements. Training and evaluation are first conducted on well-calibrated SDO images. We then extend the evaluation to raw and uncalibrated SDO images affected by instrumental artifacts. Performance is measured using the Intersection over Union (IoU) and Dice score. Results show that while SCSS-Net achieves the highest segmentation quality, YOLOv8n offers a strong balance between accuracy and efficiency. The BCVO pipeline remains viable under strict hardware limitations. Interestingly, our models retain compatibility on Level-0 observations. This is the first study comparing these widely used methods from the perspective of onboard deployment. Our findings provide a foundation for designing frameworks tailored to onboard hardware configurations.

2025

1. Classifying Solar Flares with kNN on Sparse Autoencoder Representations

   Hanne Baeke, G Miloshevich, E Dineva, and 4 more authors

   Monthly Notices of the Royal Astronomical Society, 2025
2. Prediction of Time-evolving Radial Magnetic Fields on the Solar Surface Using Deep Learning

   Hyun-Jin Jeong, Stefaan Poedts, Haopeng Wang, and 6 more authors

   The Astrophysical Journal Letters, 2025

   Publisher: IOP Publishing
3. Efficient Segmentation and Clustering of Solar Coronal Structures: A Comparison of U-Net and Classical Computer Vision Techniques Using SDO Data

   Panagiotis Gonidakis, Francesco Carella, George Miloshevich, and 1 more author

   In EGU General Assembly Conference Abstracts, 2025
4. Machine Learning Algorithms for Autonomous Space Mission Operations

   Tommaso Torda, Tommaso Alberti, Giuseppe Consolini, and 7 more authors

   In EGU General Assembly Conference Abstracts, 2025

2024

1. The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data

   Luuk H. Boulogne, Julian Lorenz, Daniel Kienzle, and 7 more authors

   Medical Image Analysis, 2024

   Publisher: Elsevier
2. Data-and label-efficient deep learning for medical image analysis: Application to lung nodule detection on thoracic CT

   Panagiotis Gonidakis

   2024

2023

1. Handcrafted features can boost performance and data-efficiency for deep detection of lung nodules from CT imaging

   Panagiotis Gonidakis, Alexander Sóñora-Mengana, Bart Jansen, and 1 more author

   Ieee Access, 2023

   Publisher: IEEE

2022

1. Comparative study of deep learning methods for the automatic segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients

   Sofie Tilborghs, Ine Dirks, Lucas Fidon, and 19 more authors

   Jan 2022

   arXiv:2007.15546 [eess]

   Abs DOI

   Recent research on COVID-19 suggests that CT imaging provides useful information to assess disease progression and assist diagnosis, in addition to help understanding the disease. There is an increasing number of studies that propose to use deep learning to provide fast and accurate quantification of COVID-19 using chest CT scans. The main tasks of interest are the automatic segmentation of lung and lung lesions in chest CT scans of confirmed or suspected COVID-19 patients. In this study, we compare twelve deep learning algorithms using a multi-center dataset, including both open-source and in-house developed algorithms. Results show that ensembling different methods can boost the overall test set performance for lung segmentation, binary lesion segmentation and multiclass lesion segmentation, resulting in mean Dice scores of 0.982, 0.724 and 0.469, respectively. The resulting binary lesions were segmented with a mean absolute volume error of 91.3 ml. In general, the task of distinguishing different lesion types was more difficult, with a mean absolute volume difference of 152 ml and mean Dice scores of 0.369 and 0.523 for consolidation and ground glass opacity, respectively. All methods perform binary lesion segmentation with an average volume error that is better than visual assessment by human raters, suggesting these methods are mature enough for a large-scale evaluation for use in clinical practice.

2021

1. Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging

   Abel Díaz Berenguer, Hichem Sahli, Boris Joukovsky, and 37 more authors

   Sep 2021

   arXiv:2011.11719 [eess]

   Abs DOI

   Our motivating application is a real-world problem: COVID-19 classification from CT imaging, for which we present an explainable Deep Learning approach based on a semi-supervised classification pipeline that employs variational autoencoders to extract efficient feature embedding. We have optimized the architecture of two different networks for CT images: (i) a novel conditional variational autoencoder (CVAE) with a specific architecture that integrates the class labels inside the encoder layers and uses side information with shared attention layers for the encoder, which make the most of the contextual clues for representation learning, and (ii) a downstream convolutional neural network for supervised classification using the encoder structure of the CVAE. With the explainable classification results, the proposed diagnosis system is very effective for COVID-19 classification. Based on the promising results obtained qualitatively and quantitatively, we envisage a wide deployment of our developed technique in large-scale clinical studies.Code is available at https://git.etrovub.be/AVSP/ct-based-covid-19-diagnostic-tool.git.

2020

1. Evaluating several ways to combine handcrafted features-based system with a deep learning system using the LUNA16 Challenge framework

   Alexander Sóñora-Mengan, Panagiotis Gonidakis, Bart Jansen, and 2 more authors

   In Medical Imaging 2020: Computer-Aided Diagnosis, Sep 2020
2. Artificially augmenting data or adding more samples? A study on a 3D CNN for lung nodule classification

   Panagiotis Gonidakis, Bart Jansen, and Jef Vandemeulebroucke

   In Medical Imaging 2020: Computer-Aided Diagnosis, Sep 2020
3. Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging

   Abel Díaz Berenguer, Hichem Sahli, Boris Joukovsky, and 7 more authors

   arXiv e-prints, Sep 2020

2018

1. Computer-Aided Detection of Lung Nodules using Multi-Level Contextual 3D CNNs

   Panagiotis Gonidakis, Lubos Omelina, Bart Jansen, and 1 more author

   Sep 2018
2. Fossil hominids on the move: new developments in fossil hominid biomechanical analysis.

   T. Chapman, S. Sint Jan, V. Sholukha, and 7 more authors

   In ESHE meeting, Sep 2018