output
20202026
most citedTowards defining reference materials for extracellular vesicle size, concentration, refractive index and epitope abundance

150 citations

Showing 2023Show all

12 papers · 1 filter

stat.ME2023★ 4 cited

Risk-based decision making: estimands for sequential prediction under interventions

Kim Luijken, Paweł Morzywołek, Wouter van Amsterdam +14

Prediction models are used amongst others to inform medical decisions on interventions. Typically, individuals with high risks of adverse outcomes are advised to undergo an interve…

q-bio.TO2023★ 6 cited

An open-source, three-dimensional growth model of the mandible

Cornelis Klop, Ruud Schreurs, Guido A De Jong +10

The available reference data for the mandible and mandibular growth consists primarily of two-dimensional linear or angular measurements. The aim of this study was to create the fi…

eess.IV2023★ 5 cited

Predicting Age from White Matter Diffusivity with Residual Learning

Chenyu Gao, Michael E. Kim, Ho Hin Lee +17

Imaging findings inconsistent with those expected at specific chronological age ranges may serve as early indicators of neurological disorders and increased mortality risk. Estimat…

eess.IV2023★ 6 cited

TabAttention: Learning Attention Conditionally on Tabular Data

Michal K. Grzeszczyk, Szymon Płotka, Beata Rebizant +6

Medical data analysis often combines both imaging and tabular data processing using machine learning algorithms. While previous studies have investigated the impact of attention me…

stat.ME2023★ 2 cited

A framework for interpretation and testing of sparse canonical correlations

Nuria Senar, Mark van de Wiel, Aeilko Zwinderman +1

In clinical and biomedical research, multiple high-dimensional datasets are nowadays routinely collected from omics and imaging devices. Multivariate methods, such as Canonical Cor…

eess.IV2023★ 31 cited

Robust deformable image registration using cycle-consistent implicit representations

Louis D. van Harten, Jaap Stoker, Ivana Išgum

Recent works in medical image registration have proposed the use of Implicit Neural Representations, demonstrating performance that rivals state-of-the-art learning-based methods.…