1 citations · 1 across the 6 of their papers we have counts for
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Curriculum Learning for Few-Shot Domain Adaptation in CT-based Airway Tree Segmentation
Maxime Jacovella, Ali Keshavarzi, Elsa Angelini
Despite advances with deep learning (DL), automated airway segmentation from chest CT scans continues to face challenges in segmentation quality and generalization across cohorts.…
Dense Self-Supervised Learning for Medical Image Segmentation
Maxime Seince, Loic Le Folgoc, Luiz Augusto Facury de Souza +1
Deep learning has revolutionized medical image segmentation, but it relies heavily on high-quality annotations. The time, cost and expertise required to label images at the pixel-l…
Few-Shot Airway-Tree Modeling using Data-Driven Sparse Priors
Ali Keshavarzi, Elsa Angelini
The lack of large annotated datasets in medical imaging is an intrinsic burden for supervised Deep Learning (DL) segmentation models. Few-shot learning approaches are cost-effectiv…