Predicting the Need for Adaptive Radiotherapy in Head and Neck Patients from CT-Based Radiomics and Pre-treatment Data. - Natália Alves; Joana Dias; Tiago Ventura; Josefina Mateus; Miguel Capela; Leila Khouri; Maria do Carmo Lopes (ICCSA (5), 2021)

Fully Automatic Deep Learning Framework for Pancreatic Ductal Adenocarcinoma Detection on Computed Tomography. - Natália Alves; Megan Schuurmans; Geke Litjens; Joeran S. Bosma; John Hermans; Henkjan Huisman (CoRR, 2021)

Uncertainty-Guided Self-learning Framework for Semi-supervised Multi-organ Segmentation. - Natália Alves; Bram De Wilde (FLARE@MICCAI, 2022)

Reproducibility of Training Deep Learning Models for Medical Image Analysis. - Joeran Bosma; Dre Peeters; Natália Alves; Anindo Saha; Zaigham Saghir; Colin Jacobs; Henkjan Huisman (MIDL, 2023)

Unleashing the Strengths of Unlabeled Data in Pan-cancer Abdominal Organ Quantification: the FLARE22 Challenge. - Jun Ma 016; Yao Zhang 010; Song Gu; Cheng Ge; Shihao Ma; Adamo Young; Cheng Zhu; Kangkang Meng; Xin Yang 009; Ziyan Huang; Fan Zhang; Wentao Liu; et al. (CoRR, 2023)

The ULS23 Challenge Public Training Dataset. - Max De Grauw; Natália Alves; Megan Schuurmans; Henkjan Huisman; Bram van Ginneken; Alessa Hering (Zenodo, 2023)

The DRAGON benchmark for clinical NLP. - Joeran S. Bosma; Koen Dercksen; Luc Builtjes; Romain André; Christian Roest; Stefan J. Fransen; Constant R. Noordman; Mar Navarro-Padilla; Judith Lefkes; Natália Alves; Max J. J. de Grauw; Leander van Eekelen; et al. (npj Digit. Medicine, 2025)