Artificially augmenting data or adding more samples? A study on a 3D CNN for lung nodule classification. - Panagiotis Gonidakis; Bart Jansen 001; Jef Vandemeulebroucke (Computer-Aided Diagnosis, 2020)

Evaluating several ways to combine handcrafted features-based system with a deep learning system using the LUNA16 Challenge framework. - Alexander Sóñora-Mengana; Panagiotis Gonidakis; Bart Jansen 001; Juan Carlos García-Naranjo; Jef Vandemeulebroucke (Computer-Aided Diagnosis, 2020)

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; Siri Willems; Tom Eelbode; Jeroen Bertels; Bart Ilsen; Arne Brys; Adriana Dubbeldam; Nico Buls; Panagiotis Gonidakis; Sebastián Amador Sánchez; et al. (CoRR, 2020)

Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging. - Abel Díaz Berenguer; Hichem Sahli; Boris Joukovsky; Maryna Kvasnytsia; Ine Dirks; Mitchel Alioscha-Pérez; Nikos Deligiannis; Panagiotis Gonidakis; Sebastián Amador Sánchez; Redona Brahimetaj; Evgenia Papavasileiou; Jonathan Cheung-Wai Chan; et al. (CoRR, 2020)

Handcrafted Features Can Boost Performance and Data-Efficiency for Deep Detection of Lung Nodules From CT Imaging. - Panagiotis Gonidakis; Alexander Sóñora-Mengana; Bart Jansen 001; Jef Vandemeulebroucke (IEEE Access, 2023)

The STOIC2021 COVID-19 AI challenge: Applying reusable training methodologies to private data. - Luuk H. Boulogne; Julian Lorenz; Daniel Kienzle; Robin Schön; Katja Ludwig; Rainer Lienhart; Simon Jégou; Guang Li; Cong Chen; Qi Wang; Derik Shi; Mayug Maniparambil; et al. (Medical Image Anal., 2024)