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pro vyhledávání: '"Llambias, Sebastian"'
This study investigates the impact of self-supervised pretraining of 3D semantic segmentation models on a large-scale, domain-specific dataset. We introduce BRAINS-45K, a dataset of 44,756 brain MRI volumes from public sources, the largest public dat
Externí odkaz:
http://arxiv.org/abs/2408.00640
Autor:
Llambias, Sebastian Nørgaard, Machnio, Julia, Munk, Asbjørn, Ambsdorf, Jakob, Nielsen, Mads, Ghazi, Mostafa Mehdipour
Medical image analysis using deep learning frameworks has advanced healthcare by automating complex tasks, but many existing frameworks lack flexibility, modularity, and user-friendliness. To address these challenges, we introduce Yucca, an open-sour
Externí odkaz:
http://arxiv.org/abs/2407.19888
Deep learning-based models in medical imaging often struggle to generalize effectively to new scans due to data heterogeneity arising from differences in hardware, acquisition parameters, population, and artifacts. This limitation presents a signific
Externí odkaz:
http://arxiv.org/abs/2308.04395