80 citations · 148 across the 24 of their papers we have counts for
14 papers · 1 filter
A Visual Grounding Benchmark for Retinal OCT with Multimodal Large Language Models
Ronald Fecso, José Morano, Botond Fazekas +6
Multimodal large language models (MLLMs) can answer clinical questions about medical images, but trustworthy use requires visual grounding: models should localize the pathology the…
FunPiQ: A New Benchmark for Pixel-Level Quality Assessment in Fundus Images
Pengwei Wang, José Morano, Virginia Mares +1
Color fundus photography (CFP) is the most common ophthalmic imaging modality for large-scale screening. However, it is highly susceptible to degradations, making robust fundus ima…
Test-Time Adaptation in Optical Coherence Tomography Using Trajectory-Aligned Time-Independent Flow
Veit Hucke, Thomas Pinetz, Gregor Reiter +2
Optical coherence tomography (OCT) is essential in ophthalmology, but inconsistent image quality especially in low-cost devices hinders automated analysis. To address this, we intr…
EFIQA: Explainable Fundus Image Quality Assessment via Anatomical Priors
Pengwei Wang, José Morano, Qian Wan +1
Image quality control is vital for a wide range of downstream applications. Deep learning-based image quality assessment methods typically train classifiers on dataset-specific qua…
Quantification of Uncertainty with Adversarial Models in Medical Image Segmentation
Hana Jebril, Thomas Pinetz, Günter Klambauer +1
Reliable pixel-level uncertainty quantification holds the potential to transform clinical workflows by enabling high-fidelity longitudinal monitoring and distinguishing true pathol…
PSScreen V2: Partially Supervised Multiple Retinal Disease Screening
Boyi Zheng, Yalin Zheng, Hrvoje Bogunović +1
In this work, we propose PSScreen V2, a partially supervised self-training framework for multiple retinal disease screening. Unlike previous methods that rely on fully labelled or…
PiPViT: Patch-based Visual Interpretable Prototypes for Retinal Image Analysis
Marzieh Oghbaie, Teresa Araújo, Hrvoje Bogunović
Background and Objective: Prototype-based methods improve interpretability by learning fine-grained part-prototypes; however, their visualization in the input pixel space is not al…
MIRAGE: Multimodal foundation model and benchmark for comprehensive retinal OCT image analysis
José Morano, Botond Fazekas, Emese Sükei +7
Artificial intelligence (AI) has become a fundamental tool for assisting clinicians in analyzing ophthalmic images, such as optical coherence tomography (OCT). However, developing…
Deep Learning for Retinal Degeneration Assessment: A Comprehensive Analysis of the MARIO Challenge
Rachid Zeghlache, Ikram Brahim, Pierre-Henri Conze +47
The MARIO challenge, held at MICCAI 2024, focused on advancing the automated detection and monitoring of age-related macular degeneration (AMD) through the analysis of optical cohe…
Forecasting Disease Progression with Parallel Hyperplanes in Longitudinal Retinal OCT
Arunava Chakravarty, Taha Emre, Dmitrii Lachinov +8
Predicting future disease progression risk from medical images is challenging due to patient heterogeneity, and subtle or unknown imaging biomarkers. Moreover, deep learning (DL) m…
Learning Temporally Equivariance for Degenerative Disease Progression in OCT by Predicting Future Representations
Taha Emre, Arunava Chakravarty, Dmitrii Lachinov +3
Contrastive pretraining provides robust representations by ensuring their invariance to different image transformations while simultaneously preventing representational collapse. E…
Spatiotemporal Representation Learning for Short and Long Medical Image Time Series
Chengzhi Shen, Martin J. Menten, Hrvoje Bogunović +7
Analyzing temporal developments is crucial for the accurate prognosis of many medical conditions. Temporal changes that occur over short time scales are key to assessing the health…
Pretrained Deep 2.5D Models for Efficient Predictive Modeling from Retinal OCT
Taha Emre, Marzieh Oghbaie, Arunava Chakravarty +9
In the field of medical imaging, 3D deep learning models play a crucial role in building powerful predictive models of disease progression. However, the size of these models presen…
TINC: Temporally Informed Non-Contrastive Learning for Disease Progression Modeling in Retinal OCT Volumes
Taha Emre, Arunava Chakravarty, Antoine Rivail +3
Recent contrastive learning methods achieved state-of-the-art in low label regimes. However, the training requires large batch sizes and heavy augmentations to create multiple view…