Deep learning methods have been very effective for a variety of medical diagnostic tasks
and have even outperformed human experts on some of those. However, the black-box …

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 …

Data-driven computational approaches have evolved to enable extraction of information
from medical images with reliability, accuracy, and speed, which is already transforming …

Recent technological developments have resulted in the availability of miniaturised spectral
imaging sensors capable of operating in the multi-(MSI) and hyperspectral imaging (HSI) …

Constraining geophysical models with observed data usually involves solving nonlinear and
nonunique inverse problems. Neural mixture density networks (MDNs) provide an efficient …

Although for many diseases there is a progressive diagnosis scale, automatic analysis of
grade-based medical images is quite often addressed as a binary classification problem …

Laparoscopic surgery has evolved as a key technique for cancer diagnosis and therapy.
While characterization of the tissue perfusion is crucial in various procedures, such as partial …

The comprehensive integration of machine learning healthcare models within clinical
practice remains suboptimal, notwithstanding the proliferation of high-performing solutions …

Coupling-based normalizing flows (eg RealNVP) are a popular family of normalizing flow
architectures that work surprisingly well in practice. This calls for theoretical understanding …

Synthetic medical image generation has evolved as a key technique for neural network
training and validation. A core challenge, however, remains in the domain gap between …