Biblio
Beyond ImageNet: Deep Learning in Industrial Practice.
Applied Data Science. 205-232.
(2019). Automatic classification of non-small cell lung cancer histologic sub-types by deep learning.
VIRCHOWS ARCHIV. 108-108.
(2018). Deep transformation models: Tackling complex regression problems with neural network based transformation models.
Accepted for Proceedings of the 25th International Conference on Pattern Recognition (ICPR), Milan/Online, 2021.
(2021). Probabilistic short-term low-voltage load forecasting using bernstein-polynomial normalizing flows.
ICML 2021, Workshop Tackling Climate Change with Machine Learning, June 26, 2021, virtual.
(2021). Deep and interpretable regression models for ordinal outcomes.
Pattern Recognition. 122, 108263.
(2022). Deep transformation models for functional outcome prediction after acute ischemic stroke.
Biometrical Journal. 65, 2100379.
(2023). Developing deep learning applications for life science and pharma industry.
Drug research. 68, 305–310.
(2018). Integrating uncertainty in deep neural networks for MRI based stroke analysis.
Medical Image Analysis. 65, 101790.
(2020). Know When You Don't Know: A Robust Deep Learning Approach in the Presence of Unknown Phenotypes.
Assay and drug development technologies. 16, 343–349. adt.2018.859.pdf (711.06 KB)
(2018). Short-term density forecasting of low-voltage load using Bernstein-polynomial normalizing flows.
IEEE Transactions on Smart Grid.
(2023). Single-Cell Phenotype Classification Using Deep Convolutional Neural Networks.
Journal of biomolecular screening. 21, 998–1003.
(2016).