Highlights•Review of the state-of-the-art in uncertainty quantification in drug
design.•Examples from drug-design settings are provided.•Impact on decision making is …

Over the last decade, neural networks have reached almost every field of science and
become a crucial part of various real world applications. Due to the increasing spread …

There have been considerable debates over 2D and 3D representation learning on 3D
medical images. 2D approaches could benefit from large-scale 2D pretraining, whereas they …

Machine learning (ML) plays a growing role in the design and discovery of chemicals,
aiming to reduce the need to perform expensive experiments and simulations. ML for such …

Uncertainty Quantification (UQ) has gained traction in an attempt to fix the black-box nature
of Deep Learning. Specifically (medical) biosignals such as electroencephalography (EEG) …

This paper addresses a fundamental challenge in 3D medical image processing: how to
deal with imaging thickness. For anisotropic medical volumes, there is a significant …

Diagnosis of pulmonary lesions from computed tomography (CT) is important but
challenging for clinical decision making in lung cancer related diseases. Deep learning has …

Uncertainty Quantification aims to determine when the prediction from a Machine Learning
model is likely to be wrong. Computer Vision research has explored methods for …

Bayesian neural networks (BNNs) have become a principal approach to alleviate
overconfident predictions in deep learning, but they often suffer from scaling issues due to a …

WWee eevvaalluuaattee 33 ssttaattee--ooff--tthhee--aarrtt tteecchhnniiqquueess ffoorr
uunncceerrttaaiinnttyy qquuaannttiiggccaattiioonn:: MMoonnttee--CCaarrlloo …