7 citations · 10 across the 9 of their papers we have counts for
9 papers
AMAES: Augmented Masked Autoencoder Pretraining on Public Brain MRI Data for 3D-Native Segmentation
Asbjørn Munk, Jakob Ambsdorf, Sebastian Llambias +1
This study investigates the impact of self-supervised pretraining of 3D semantic segmentation models on a large-scale, domain-specific dataset. We introduce BRAINS-45K, a dataset o…
Unsupervised Detection of Fetal Brain Anomalies using Denoising Diffusion Models
Markus Ditlev Sjøgren Olsen, Jakob Ambsdorf, Manxi Lin +7
Congenital malformations of the brain are among the most common fetal abnormalities that impact fetal development. Previous anomaly detection methods on ultrasound images are based…
Yucca: A Deep Learning Framework For Medical Image Analysis
Sebastian Nørgaard Llambias, Julia Machnio, Asbjørn Munk +3
Medical image analysis using deep learning frameworks has advanced healthcare by automating complex tasks, but many existing frameworks lack flexibility, modularity, and user-frien…
Learning semantic image quality for fetal ultrasound from noisy ranking annotation
Manxi Lin, Jakob Ambsdorf, Emilie Pi Fogtmann Sejer +9
We introduce the notion of semantic image quality for applications where image quality relies on semantic requirements. Working in fetal ultrasound, where ranking is challenging an…
Local Gamma Augmentation for Ischemic Stroke Lesion Segmentation on MRI
Jon Middleton, Marko Bauer, Kaining Sheng +5
The identification and localisation of pathological tissues in medical images continues to command much attention among deep learning practitioners. When trained on abundant datase…
Data Augmentation-Based Unsupervised Domain Adaptation In Medical Imaging
Sebastian Nørgaard Llambias, Mads Nielsen, Mostafa Mehdipour Ghazi
Deep learning-based models in medical imaging often struggle to generalize effectively to new scans due to data heterogeneity arising from differences in hardware, acquisition para…