End-to-End Cascaded U-Nets with a Localization Network for Kidney Tumor Segmentation. - Minh H. Vu; Guus Grimbergen; Attila Simkó; Tufve Nyholm; Tommy Löfstedt (CoRR, 2019)

Changing the Contrast of Magnetic Resonance Imaging Signals using Deep Learning. - Attila Tibor Simkó; Tommy Löfstedt; Anders Garpebring; Mikael Bylund; Tufve Nyholm; Joakim Jonsson (MIDL, 2021)

MRI bias field correction with an implicitly trained CNN. - Attila Simkó; Tommy Löfstedt; Anders Garpebring; Tufve Nyholm; Joakim Jonsson (MIDL, 2022)

Reproducibility of the Methods in Medical Imaging with Deep Learning. - Attila Simkó; Anders Garpebring; Joakim Jonsson; Tufve Nyholm; Tommy Löfstedt (MIDL, 2023)

Improving MR image quality with a multi-task model, using convolutional losses. - Attila Simkó; Simone Ruiter; Tommy Löfstedt; Anders Garpebring; Tufve Nyholm; Mikael Bylund; Joakim Jonsson (BMC Medical Imaging, 2023)

From Promise to Practice: A Study of Common Pitfalls Behind the Generalization Gap in Machine Learning. - Saeideh Ghanbari Azar; Lorenzo Tronchin; Attila Simkó; Tufve Nyholm; Tommy Löfstedt (Trans. Mach. Learn. Res., 2025)