1 citations · 1 across the 6 of their papers we have counts for
6 papers
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…
Boundary-Emphasized Weight Maps for Distal Airway Segmentation
Ali Keshavarzi, Elsa Angelini
Automated airway segmentation from lung CT scans is vital for diagnosing and monitoring pulmonary diseases. Despite advancements, challenges like leakage, breakage, and class imbal…
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…