The medical imaging literature has witnessed remarkable progress in high-performing
segmentation models based on convolutional neural networks. Despite the new …

The scarcity of high-quality annotated medical imaging datasets is a major problem that
collides with machine learning applications in the field of medical imaging analysis and …

Telesurgical robotics, as a technical solution for robot-assisted minimally invasive surgery
(RAMIS), has become the first domain within medicosurgical robotics that achieved a true …

Generative adversarial networks have gained a lot of attention in the computer vision
community due to their capability of data generation without explicitly modelling the …

Recent developments in data science in general and machine learning in particular have
transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a …

Abstract Machine learning (ML) algorithms have made a tremendous impact in the field of
medical imaging. While medical imaging datasets have been growing in size, a challenge …

Photoacoustic imaging (PAI) is a promising emerging imaging modality that enables
spatially resolved imaging of optical tissue properties up to several centimeters deep in …

Transfer learning from natural images to medical images has been established as one of the
most practical paradigms in deep learning for medical image analysis. To fit this paradigm …

Self-supervised learning methods have witnessed a recent surge of interest after proving
successful in multiple application fields. In this work, we leverage these techniques, and we …

The scarcity of labeled data often limits the application of supervised deep learning
techniques for medical image segmentation. This has motivated the development of semi …