most citedTowards Investigating Residual Hearing Loss: Quantification of Fibrosis in a Novel Cochlear OCT Dataset

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cs.CV20262 cited

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 (…

cs.CV2026

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 access…

cs.CV2026

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…

cs.CV2025

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…

cs.CV2024

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…