← Search

Konstantin Nikolaou

4 accepted papers

2026

Spectral Reach: Understanding Neural Scaling through Kernel Alignment Dynamics

ICML 2026poster

Neural scaling laws describe predictable power-law relationships between model size, dataset size, compute cost, and performance. While these laws are applied to improve the performance of modern foundation models, the mechanisms underpinning them are less understood, in part due to the absence of s…

Cited by 0SourceScholar
2021

Automated Multi-Organ Segmentation in Pet Images Using Cascaded Training of a 3d U-Net and Convolutional Autoencoder

ICASSP 2021accepted

PET imaging is an important tool in clinical diagnostics, especially in oncology as it is able to visualize ongoing metabolic processes, e.g. caused by a tumor. Due to the low spatial resolution, a corresponding CT or MRI scan is normally necessary to gain knowledge about the physiological structure…

Cited by 0SourceScholar
2018

Automated Detection of High FDG Uptake Regions in CT Images

ICASSP 2018accepted

Combined PET-CT scan is an important diagnostic tool in modern medicine, e.g. for staging or treatment planning in the field of oncology. Especially in small structures, like a tumour, textural variations visible in a PET image are not visually recognizable within a CT scan from the same region. Thu…

Cited by 0SourceScholar
2018

Automatic Motion Artifact Detection for Whole-Body Magnetic Resonance Imaging

ICASSP 2018accepted

Magnetic resonance (MR) plays an important role in medical imaging. It can be flexibly tuned towards different applications for deriving a meaningful diagnosis. However, its long acquisition times and flexible parametrization make it on the other hand prone to artifacts which obscure the underlying…

Cited by 0SourceScholar