2 citations · 4 across the 8 of their papers we have counts for
5 papers · 1 filter
Beyond Training from Scratch: Foundation Models for Data-Efficient and Generalizable Cardiac MRI Reconstruction
Anam Hashmi, Mayug Maniparambil, Julia Dietlmeier +2
Cardiac magnetic resonance imaging reconstruction aims to recover high-quality images from undersampled acquisitions, enabling faster scans while preserving diagnostic fidelity. Re…
Towards Investigating Residual Hearing Loss: Quantification of Fibrosis in a Novel Cochlear OCT Dataset
Julia Dietlmeier, Benjamin Greenberg, Wenxuan He +10
Objective: Cochlear implants (CIs) are bionic prostheses that restores hearing via electrical stimulation of the auditory nerve. Hybrid CIs, which use electroacoustic stimulation (…
Are Natural-Domain Foundation Models Effective for Accelerated Cardiac MRI Reconstruction?
Anam Hashmi, Mayug Maniparambil, Julia Dietlmeier +2
The emergence of large-scale pretrained foundation models has transformed computer vision, enabling strong performance across diverse downstream tasks. However, their potential for…
Cross-Modal Knowledge Distillation for PET-Free Amyloid-Beta Detection from MRI
Francesco Chiumento, Julia Dietlmeier, Ronan P. Killeen +3
Detecting amyloid- (A) positivity is crucial for early diagnosis of Alzheimer's disease but typically requires PET imaging, which is costly, invasive, and not widely accessib…
Ensemble Learning with Sparse Hypercolumns
Julia Dietlmeier, Vayangi Ganepola, Oluwabukola G. Adegboro +3
Directly inspired by findings in biological vision, high-dimensional hypercolumns are feature vectors built by concatenating multi-scale activations of convolutional neural network…