2 citations · 4 across the 8 of their papers we have counts for
6 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 (…
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…
Towards Label-Free Brain Tumor Segmentation: Unsupervised Learning with Multimodal MRI
Gerard Comas-Quiles, Carles Garcia-Cabrera, Julia Dietlmeier +2
Unsupervised anomaly detection (UAD) presents a complementary alternative to supervised learning for brain tumor segmentation in magnetic resonance imaging (MRI), particularly when…
Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero shot Medical Image Segmentation
Sidra Aleem, Fangyijie Wang, Mayug Maniparambil +6
The Segment Anything Model (SAM) and CLIP are remarkable vision foundation models (VFMs). SAM, a prompt driven segmentation model, excels in segmentation tasks across diverse domai…