1 citations · 1 across the 6 of their papers we have counts for
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Pix2Rep-v2: Data-Efficient Representation Learning for Dense Medical Imaging Applications
S. Sifaoui, E. Angelini, S. Toupin +2
Dense self-supervised learning (SSL) is a powerful paradigm for learning without annotations the local descriptors required to solve dense medical imaging tasks. We present Pix2Rep…
Leveraging whole slide difficulty in Multiple Instance Learning to improve prostate cancer grading
Marie Arrivat, Rémy Peyret, Elsa Angelini +1
Multiple Instance Learning (MIL) has been widely applied in histopathology to classify Whole Slide Images (WSIs) with slide-level diagnoses. While the ground truth is established b…
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…