Radiation therapy planning and simulation with magnetic resonance images. - Thomas Boettger; Tufve Nyholm; Magnus Karlsson; Chandrasekhar Nunna; Juan Carlos Celi (Image-Guided Procedures, 2008)

TuNet: End-to-End Hierarchical Brain Tumor Segmentation Using Cascaded Networks. - Minh H. Vu; Tufve Nyholm; Tommy Löfstedt (BrainLes@MICCAI (1), 2019)

Ensemble of Streamlined Bilinear Visual Question Answering Models for the ImageCLEF 2019 Challenge in the Medical Domain. - Minh H. Vu; Raphael Sznitman; Tufve Nyholm; Tommy Löfstedt (CLEF (Working Notes), 2019)

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)

Evaluation of Multi-Slice Inputs to Convolutional Neural Networks for Medical Image Segmentation. - Minh H. Vu; Guus Grimbergen; Tufve Nyholm; Tommy Löfstedt (CoRR, 2019)

Multi-decoder Networks with Multi-denoising Inputs for Tumor Segmentation. - Minh H. Vu; Tufve Nyholm; Tommy Löfstedt (BrainLes@MICCAI (1), 2020)

A Question-Centric Model for Visual Question Answering in Medical Imaging. - Minh H. Vu; Tommy Löfstedt; Tufve Nyholm; Raphael Sznitman (IEEE Trans. Medical Imaging, 2020)

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)

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Metrics and Benchmarking Results. - Raghav Mehta; Angelos Filos; Ujjwal Baid; Chiharu Sako; Richard McKinley; Michael Rebsamen; Katrin Dätwyler; Raphael Meier; Piotr Radojewski; Gowtham Krishnan Murugesan; Sahil S. Nalawade; Chandan Ganesh; et al. (CoRR, 2021)

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

Region of Interest focused MRI to Synthetic CT Translation using Regression and Classification Multi-task Network. - Sandeep Kaushik; Mikael Bylund; Cristina Cozzini; Dattesh Shanbhag; Steven F. Petit; Jonathan J. Wyatt; Marion I. Menzel; Carolin M. Pirkl; Bhairav Mehta; Vikas Chauhan; Kesavadas Chandrasekharan; Joakim Jonsson; et al. (CoRR, 2022)

A Data-Adaptive Loss Function for Incomplete Data and Incremental Learning in Semantic Image Segmentation. - Minh H. Vu; Gabriella Norman; Tufve Nyholm; Tommy Löfstedt (IEEE Trans. Medical Imaging, 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)

Compressing the Activation Maps in Deep Convolutional Neural Networks and Its Regularizing Effect. - Minh H. Vu; Anders Garpebring; Tufve Nyholm; Tommy Löfstedt (Trans. Mach. Learn. Res., 2024)

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)

Using Synthetic Images to Augment Small Medical Image Datasets. - Minh H. Vu; Lorenzo Tronchin; Tufve Nyholm; Tommy Löfstedt (CoRR, 2025)