4 citations · 14 across the 10 of their papers we have counts for
10 papers
A meaningful prediction of functional decline in amyotrophic lateral sclerosis based on multi-event survival analysis
Christian Marius Lillelund, Sanjay Kalra, Russell Greiner
Amyotrophic lateral sclerosis (ALS) is a degenerative disorder of the motor neurons that causes progressive paralysis in patients. Current treatment options aim to prolong survival…
Position: Stop Chasing the C-index when Evaluating Survival Analysis Models
Christian Marius Lillelund, Shi-ang Qi, Russell Greiner +1
The current state of evaluation in survival analysis is plagued by the persistent use of evaluation metrics in ways that are misaligned with the stated modeling objective. In addit…
Overcoming Dependent Censoring in the Evaluation of Survival Models
Christian Marius Lillelund, Shi-ang Qi, Russell Greiner
Dependent censoring occurs when the event time and censoring time are not conditionally independent given the observed covariates. This complicates survival model evaluation becaus…
MENSA: A Multi-Event Network for Survival Analysis with Trajectory-based Likelihood Estimation
Christian Marius Lillelund, Ali Hossein Gharari Foomani, Weijie Sun +2
Most existing time-to-event methods focus on either single-event or competing-risks settings, leaving multi-event scenarios relatively underexplored. In many healthcare application…
An Explainable and Conformal AI Model to Detect Temporomandibular Joint Involvement in Children Suffering from Juvenile Idiopathic Arthritis
Lena Todnem Bach Christensen, Dikte Straadt, Stratos Vassis +5
Juvenile idiopathic arthritis (JIA) is the most common rheumatic disease during childhood and adolescence. The temporomandibular joints (TMJ) are among the most frequently affected…
RULSurv: A probabilistic survival-based method for early censoring-aware prediction of remaining useful life in ball bearings
Christian Marius Lillelund, Fernando Pannullo, Morten Opprud Jakobsen +2
Predicting the remaining useful life (RUL) of ball bearings is an active area of research, where novel machine learning techniques are continuously being applied to predict degrada…
Efficient Training of Probabilistic Neural Networks for Survival Analysis
Christian Marius Lillelund, Martin Magris, Christian Fischer Pedersen
Variational Inference (VI) is a commonly used technique for approximate Bayesian inference and uncertainty estimation in deep learning models, yet it comes at a computational cost,…
Predicting Survival Time of Ball Bearings in the Presence of Censoring
Christian Marius Lillelund, Fernando Pannullo, Morten Opprud Jakobsen +1
Ball bearings find widespread use in various manufacturing and mechanical domains, and methods based on machine learning have been widely adopted in the field to monitor wear and s…
Cloud K-SVD for Image Denoising
Christian Marius Lillelund, Henrik Bagger Jensen, Christian Fischer Pedersen
Cloud K-SVD is a dictionary learning algorithm that can train at multiple nodes and hereby produce a mutual dictionary to represent low-dimensional geometric structures in image da…
Super-convergence and Differential Privacy: Training faster with better privacy guarantees
Osvald Frisk, Friedrich Dörmann, Christian Marius Lillelund +1
The combination of deep neural networks and Differential Privacy has been of increasing interest in recent years, as it offers important data protection guarantees to the individua…