5 papers
PU-UNet: Stable Multiplicative Interactions for Medical Image Segmentation
Ziyuan Li, Osamah Sufyan, Uwe Jaekel +1
Many dense prediction networks rely on additive feature transformations and model higher-order feature interactions only implicitly. Product units provide an explicit mechanism for…
PURe: A Plug-and-Play Product-Unit Residual Module for Vision Networks
Ziyuan Li, Uwe Jaekel, Babette Dellen
Modern vision networks are dominated by additive local transformations, whereas explicit multiplicative local interactions remain underexplored. Product units offer a direct approa…
Product units in gated recurrent units improve nuclear-mass prediction
Ziyuan Li, Paulo S. A. Freitas, John W. Clark +1
The prediction of masses of atomic nuclei using machine learning can complement theoretical models and advance the exploration of poorly known domains of the nuclear chart. We prop…
Modeling Nonlinear Feature Interactions with Product-Unit Residual Networks
Ziyuan Li, Uwe Jaekel, Babette Dellen
Understanding nonlinear feature interactions is crucial in science and engineering, yet standard multilayer perceptrons (MLPs) often capture such interactions only implicitly, lead…
Anatomy-Informed Deep Learning for Abdominal Aortic Aneurysm Segmentation
Osamah Sufyan, Martin Brückmann, Ralph Wickenhöfer +2
In CT angiography, the accurate segmentation of abdominal aortic aneurysms (AAAs) is difficult due to large anatomical variability, low-contrast vessel boundaries, and the close pr…