30 citations · 87 across the 13 of their papers we have counts for
20 papers
Effect of Prior-based Losses on Segmentation Performance: A Benchmark
Rosana El Jurdi, Caroline Petitjean, Veronika Cheplygina +2
Today, deep convolutional neural networks (CNNs) have demonstrated state-of-the-art performance for medical image segmentation, on various imaging modalities and tasks. Despite ear…
Cats, not CAT scans: a study of dataset similarity in transfer learning for 2D medical image classification
Irma van den Brandt, Floris Fok, Bas Mulders +2
Transfer learning is a commonly used strategy for medical image classification, especially via pretraining on source data and fine-tuning on target data. There is currently no cons…
Using uncertainty estimation to reduce false positives in liver lesion detection
Ishaan Bhat, Hugo J. Kuijf, Veronika Cheplygina +1
Despite the successes of deep learning techniques at detecting objects in medical images, false positive detections occur which may hinder an accurate diagnosis. We propose a techn…
High-level Prior-based Loss Functions for Medical Image Segmentation: A Survey
Rosana El Jurdi, Caroline Petitjean, Paul Honeine +2
Today, deep convolutional neural networks (CNNs) have demonstrated state of the art performance for supervised medical image segmentation, across various imaging modalities and tas…
Crowdsourcing Airway Annotations in Chest Computed Tomography Images
Veronika Cheplygina, Adria Perez-Rovira, Wieying Kuo +2
Measuring airways in chest computed tomography (CT) scans is important for characterizing diseases such as cystic fibrosis, yet very time-consuming to perform manually. Machine lea…
Primary Tumor Origin Classification of Lung Nodules in Spectral CT using Transfer Learning
Linde S. Hesse, Pim A. de Jong, Josien P. W. Pluim +1
Early detection of lung cancer has been proven to decrease mortality significantly. A recent development in computed tomography (CT), spectral CT, can potentially improve diagnosti…