80 citations · 148 across the 24 of their papers we have counts for
26 papers
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…
Stochastic Siamese MAE Pretraining for Longitudinal Medical Images
Taha Emre, Arunava Chakravarty, Thomas Pinetz +9
Temporally aware image representations are crucial for capturing disease progression in 3D volumes of longitudinal medical datasets. However, recent state-of-the-art self-supervise…
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…
Automatic detection and prediction of nAMD activity change in retinal OCT using Siamese networks and Wasserstein Distance for ordinality
Taha Emre, Teresa Araújo, Marzieh Oghbaie +3
Neovascular age-related macular degeneration (nAMD) is a leading cause of vision loss among older adults, where disease activity detection and progression prediction are critical f…
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…
Comparative Analysis of Data Augmentation for Retinal OCT Biomarker Segmentation
Markus Unterdechler, Botond Fazekas, Guilherme Aresta +1
Data augmentation plays a crucial role in addressing the challenge of limited expert-annotated datasets in deep learning applications for retinal Optical Coherence Tomography (OCT)…
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…
RRWNet: Recursive Refinement Network for effective retinal artery/vein segmentation and classification
José Morano, Guilherme Aresta, Hrvoje Bogunović
The caliber and configuration of retinal blood vessels serve as important biomarkers for various diseases and medical conditions. A thorough analysis of the retinal vasculature req…
SAMedOCT: Adapting Segment Anything Model (SAM) for Retinal OCT
Botond Fazekas, José Morano, Dmitrii Lachinov +2
The Segment Anything Model (SAM) has gained significant attention in the field of image segmentation due to its impressive capabilities and prompt-based interface. While SAM has al…
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…
Self-supervised learning via inter-modal reconstruction and feature projection networks for label-efficient 3D-to-2D segmentation
José Morano, Guilherme Aresta, Dmitrii Lachinov +3
Deep learning has become a valuable tool for the automation of certain medical image segmentation tasks, significantly relieving the workload of medical specialists. Some of these…
PALM: Open Fundus Photograph Dataset with Pathologic Myopia Recognition and Anatomical Structure Annotation
Huihui Fang, Fei Li, Junde Wu +6
Pathologic myopia (PM) is a common blinding retinal degeneration suffered by highly myopic population. Early screening of this condition can reduce the damage caused by the associa…
Morph-SSL: Self-Supervision with Longitudinal Morphing to Predict AMD Progression from OCT
Arunava Chakravarty, Taha Emre, Oliver Leingang +8
The lack of reliable biomarkers makes predicting the conversion from intermediate to neovascular age-related macular degeneration (iAMD, nAMD) a challenging task. We develop a Deep…
AIROGS: Artificial Intelligence for RObust Glaucoma Screening Challenge
Coen de Vente, Koenraad A. Vermeer, Nicolas Jaccard +33
The early detection of glaucoma is essential in preventing visual impairment. Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effectiv…
Clustering disease trajectories in contrastive feature space for biomarker discovery in age-related macular degeneration
Robbie Holland, Oliver Leingang, Christopher Holmes +13
Age-related macular degeneration (AMD) is the leading cause of blindness in the elderly. Current grading systems based on imaging biomarkers only coarsely group disease stages into…
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…