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20222026
most citedFakeNews: GAN-based generation of realistic 3D volumetric data -- A systematic review and taxonomy

45 citations · 92 across the 14 of their papers we have counts for

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7 papers · 1 filter

eess.IV2026

++nnU-Net: Scaling nnU-Net with Prefix-Based Data Augmentation

Ana Sofia Santos, André Ferreira, Gijs Luijten +6

The nnU-Net has demonstrated continuous success in medical segmentation tasks, which heavily rely on the availability and diversity of annotated biomedical data. However, assemblin…

eess.IV2025

Enhancing Privacy: The Utility of Stand-Alone Synthetic CT and MRI for Tumor and Bone Segmentation

André Ferreira, Kunpeng Xie, Caroline Wilpert +12

AI requires extensive datasets, while medical data is subject to high data protection. Anonymization is essential, but poses a challenge for some regions, such as the head, as iden…

eess.IV2024

Comparative Analysis of nnUNet and MedNeXt for Head and Neck Tumor Segmentation in MRI-guided Radiotherapy

Nikoo Moradi, André Ferreira, Behrus Puladi +5

Radiation therapy (RT) is essential in treating head and neck cancer (HNC), with magnetic resonance imaging(MRI)-guided RT offering superior soft tissue contrast and functional ima…

eess.IV2024★ 1 cited

Deep Dive into MRI: Exploring Deep Learning Applications in 0.55T and 7T MRI

Ana Carolina Alves, André Ferreira, Behrus Puladi +2

The development of magnetic resonance imaging (MRI) for medical imaging has provided a leap forward in diagnosis, providing a safe, non-invasive alternative to techniques involving…

eess.IV2024★ 1 cited

Deep PCCT: Photon Counting Computed Tomography Deep Learning Applications Review

Ana Carolina Alves, André Ferreira, Gijs Luijten +4

Medical imaging faces challenges such as limited spatial resolution, interference from electronic noise and poor contrast-to-noise ratios. Photon Counting Computed Tomography (PCCT…

eess.IV2024★ 9 cited

How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation

André Ferreira, Naida Solak, Jianning Li +4

Deep Learning is the state-of-the-art technology for segmenting brain tumours. However, this requires a lot of high-quality data, which is difficult to obtain, especially in the me…