Radiomics converts medical images into mineable data via a high-throughput extraction of
quantitative features used for clinical decision support. However, these radiomic features are …

Deep learning (DL) has become widely used for medical image segmentation in recent
years. However, despite these advances, there are still problems for which DL-based …

This paper presents an overview of the second edition of the HEad and neCK TumOR
(HECKTOR) challenge, organized as a satellite event of the 24th International Conference …

Medical image segmentation plays an irreplaceable role in computer-assisted diagnosis,
treatment planning and following-up. Collecting and annotating a large-scale dataset is …

In contrast to the fully supervised methods using pixel-wise mask labels, box-supervised
instance segmentation takes advantage of the simple box annotations, which has recently …

Today, deep convolutional neural networks (CNNs) have demonstrated state of the art
performance for supervised medical image segmentation, across various imaging modalities …

The development of semi-supervised learning techniques is essential to enhance the
generalization capacities of machine learning algorithms. Indeed, raw image data are …

In contrast to fully supervised methods using pixel-wise mask labels, box-supervised
instance segmentation takes advantage of simple box annotations, which has recently …

Image segmentation is a fundamental task in the field of imaging and vision. Supervised
deep learning for segmentation has achieved unparalleled success when sufficient training …

Abstract Background and Objective: Open-source deep learning toolkits are one of the
driving forces for developing medical image segmentation models that are essential for …