2 citations · 8 across the 13 of their papers we have counts for
16 papers
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…
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…
VLSM-Ensemble: Ensembling CLIP-based Vision-Language Models for Enhanced Medical Image Segmentation
Julia Dietlmeier, Oluwabukola Grace Adegboro, Vayangi Ganepola +2
Vision-language models and their adaptations to image segmentation tasks present enormous potential for producing highly accurate and interpretable results. However, implementation…
Parameter-Free Bio-Inspired Channel Attention for Enhanced Cardiac MRI Reconstruction
Anam Hashmi, Julia Dietlmeier, Kathleen M. Curran +1
Attention is a fundamental component of the human visual recognition system. The inclusion of attention in a convolutional neural network amplifies relevant visual features and sup…
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…
The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023
Jun Lyu, Chen Qin, Shuo Wang +47
Cardiac MRI, crucial for evaluating heart structure and function, faces limitations like slow imaging and motion artifacts. Undersampling reconstruction, especially data-driven alg…
Accelerating Cardiac MRI Reconstruction with CMRatt: An Attention-Driven Approach
Anam Hashmi, Julia Dietlmeier, Kathleen M. Curran +1
Cine cardiac magnetic resonance (CMR) imaging is recognised as the benchmark modality for the comprehensive assessment of cardiac function. Nevertheless, the acquisition process of…
ConvLoRA and AdaBN based Domain Adaptation via Self-Training
Sidra Aleem, Julia Dietlmeier, Eric Arazo +1
Existing domain adaptation (DA) methods often involve pre-training on the source domain and fine-tuning on the target domain. For multi-target domain adaptation, having a dedicated…
An L2-Normalized Spatial Attention Network For Accurate And Fast Classification Of Brain Tumors In 2D T1-Weighted CE-MRI Images
Grace Billingsley, Julia Dietlmeier, Vivek Narayanaswamy +2
We propose an accurate and fast classification network for classification of brain tumors in MRI images that outperforms all lightweight methods investigated in terms of accuracy.…
Motion Aware Self-Supervision for Generic Event Boundary Detection
Ayush K. Rai, Tarun Krishna, Julia Dietlmeier +3
The task of Generic Event Boundary Detection (GEBD) aims to detect moments in videos that are naturally perceived by humans as generic and taxonomy-free event boundaries. Modeling…
Improving Person Re-Identification with Temporal Constraints
Julia Dietlmeier, Feiyan Hu, Frances Ryan +2
In this paper we introduce an image-based person re-identification dataset collected across five non-overlapping camera views in the large and busy airport in Dublin, Ireland. Unli…
Discerning Generic Event Boundaries in Long-Form Wild Videos
Ayush K Rai, Tarun Krishna, Julia Dietlmeier +3
Detecting generic, taxonomy-free event boundaries invideos represents a major stride forward towards holisticvideo understanding. In this paper we present a technique forgeneric ev…
How important are faces for person re-identification?
Julia Dietlmeier, Joseph Antony, Kevin McGuinness +1
This paper investigates the dependence of existing state-of-the-art person re-identification models on the presence and visibility of human faces. We apply a face detection and blu…