5 papers · 1 filter
MM-DINOv2: Adapting Foundation Models for Multi-Modal Medical Image Analysis
Daniel Scholz, Ayhan Can Erdur, Viktoria Ehm +4
Vision foundation models like DINOv2 demonstrate remarkable potential in medical imaging despite their origin in natural image domains. However, their design inherently works best…
Contrastive Anatomy-Contrast Disentanglement: A Domain-General MRI Harmonization Method
Daniel Scholz, Ayhan Can Erdur, Robbie Holland +4
Magnetic resonance imaging (MRI) is an invaluable tool for clinical and research applications. Yet, variations in scanners and acquisition parameters cause inconsistencies in image…
All Sizes Matter: Improving Volumetric Brain Segmentation on Small Lesions
Ayhan Can Erdur, Daniel Scholz, Josef A. Buchner +3
Brain metastases (BMs) are the most frequently occurring brain tumors. The treatment of patients having multiple BMs with stereo tactic radiosurgery necessitates accurate localizat…
Interpretable 2D Vision Models for 3D Medical Images
Alexander Ziller, Ayhan Can Erdur, Marwa Trigui +9
Training Artificial Intelligence (AI) models on 3D images presents unique challenges compared to the 2D case: Firstly, the demand for computational resources is significantly highe…
Exploiting segmentation labels and representation learning to forecast therapy response of PDAC patients
Alexander Ziller, Ayhan Can Erdur, Friederike Jungmann +3
The prediction of pancreatic ductal adenocarcinoma therapy response is a clinically challenging and important task in this high-mortality tumour entity. The training of neural netw…