6 papers
Improved Predictive Performance and Interpretability for Mesomorphic Neural Networks Using Local Fidelity Regularization
Hugo L. Hammer, Vajira Thambawita, Kristoffer Herland Hellton +1
Interpretable Mesomorphic Neural Networks (IMNs) offer a promising framework that combines the predictive power of deep neural networks with the interpretability of linear models.…
Sampling Matters: The Effect of ECG Frequency on Deep Learning-Based Atrial Fibrillation Detection
Arjan Mahmuod, Adrian Rod Hammerstad, Muzaffar Yousef +5
Deep learning models for atrial fibrillation (AF) detection are increasingly trained on heterogeneous electrocardiogram (ECG) datasets with varying sampling frequencies, yet the sp…
The Climber's Grip -- Personalized Deep Learning Models for Fear and Muscle Activity in Climbing
Matthias Boeker, Dana Swarbrick, Ulysse T. A. Côté-Allard +3
Climbing is a multifaceted sport that combines physical demands and emotional and cognitive challenges. Ascent styles differ in fall distance with lead climbing involving larger fa…
Medical Imaging AI Competitions Lack Fairness
Annika Reinke, Evangelia Christodoulou, Sthuthi Sadananda +34
Benchmarking competitions are central to the development of artificial intelligence (AI) in medical imaging, defining performance standards and shaping methodological progress. How…
Explainability of Machine Learning Models under Missing Data
Tuan L. Vo, Thu Nguyen, Luis M. Lopez-Ramos +3
Missing data is a prevalent issue that can significantly impair model performance and explainability. This paper briefly summarizes the development of the field of missing data wit…
DPERC: Direct Parameter Estimation for Mixed Data
Tuan L. Vo, Quan Huu Do, Uyen Dang +4
The covariance matrix is a foundation in numerous statistical and machine-learning applications such as Principle Component Analysis, Correlation Heatmap, etc. However, missing val…