2 citations · 2 across the 1 of their papers we have counts for
3 papers
A Unified Framework for Tabular Generative Modeling: Loss Functions, Benchmarks, and Improved Multi-objective Bayesian Optimization Approaches
Minh H. Vu, Daniel Edler, Carl Wibom +3
Deep learning (DL) models require extensive data to achieve strong performance and generalization. Deep generative models (DGMs) offer a solution by synthesizing data. Yet current…
A physics-informed, plug-and-play dose engine for gradient-based radiotherapy treatment planning
Attila Simkó, Matthias Kronsteiner, Simon Glatzer +11
Radiotherapy treatment planning remains a time-intensive iterative process requiring expert intervention in commercial treatment planning system (TPS). While machine learning appro…
Using Synthetic Images to Augment Small Medical Image Datasets
Minh H. Vu, Lorenzo Tronchin, Tufve Nyholm +1
Recent years have witnessed a growing academic and industrial interest in deep learning (DL) for medical imaging. To perform well, DL models require very large labeled datasets. Ho…