172 citations · 422 across the 22 of their papers we have counts for
24 papers
SD-RetinaNet: Topologically Constrained Semi-Supervised Retinal Lesion and Layer Segmentation in OCT
Botond Fazekas, Guilherme Aresta, Philipp Seeböck +3
Optical coherence tomography (OCT) is widely used for diagnosing and monitoring retinal diseases, such as age-related macular degeneration (AMD). The segmentation of biomarkers suc…
GARD: Gamma-based Anatomical Restoration and Denoising for Retinal OCT
Botond Fazekas, Thomas Pinetz, Guilherme Aresta +2
Optical Coherence Tomography (OCT) is a vital imaging modality for diagnosing and monitoring retinal diseases. However, OCT images are inherently degraded by speckle noise, which o…
RetFiner: A Vision-Language Refinement Scheme for Retinal Foundation Models
Ronald Fecso, José Morano, Ursula Schmidt-Erfurth +1
The rise of imaging techniques such as optical coherence tomography (OCT) and advances in deep learning (DL) have enabled clinicians and researchers to streamline retinal disease s…
Specialized curricula for training vision-language models in retinal image analysis
Robbie Holland, Thomas R. P. Taylor, Christopher Holmes +13
Clinicians spend a significant amount of time reviewing medical images and transcribing their findings regarding patient diagnosis, referral and treatment in text form. Vision-lang…
Deep-learning-based clustering of OCT images for biomarker discovery in age-related macular degeneration (Pinnacle study report 4)
Robbie Holland, Rebecca Kaye, Ahmed M. Hagag +9
Diseases are currently managed by grading systems, where patients are stratified by grading systems into stages that indicate patient risk and guide clinical management. However, t…
Deep Multimodal Fusion of Data with Heterogeneous Dimensionality via Projective Networks
José Morano, Guilherme Aresta, Christoph Grechenig +2
The use of multimodal imaging has led to significant improvements in the diagnosis and treatment of many diseases. Similar to clinical practice, some works have demonstrated the be…
3DTINC: Time-Equivariant Non-Contrastive Learning for Predicting Disease Progression from Longitudinal OCTs
Taha Emre, Arunava Chakravarty, Antoine Rivail +10
Self-supervised learning (SSL) has emerged as a powerful technique for improving the efficiency and effectiveness of deep learning models. Contrastive methods are a prominent famil…
Transformer-based end-to-end classification of variable-length volumetric data
Marzieh Oghbaie, Teresa Araujo, Taha Emre +2
The automatic classification of 3D medical data is memory-intensive. Also, variations in the number of slices between samples is common. Naïve solutions such as subsampling can sol…
Learning Spatio-Temporal Model of Disease Progression with NeuralODEs from Longitudinal Volumetric Data
Dmitrii Lachinov, Arunava Chakravarty, Christoph Grechenig +2
Robust forecasting of the future anatomical changes inflicted by an ongoing disease is an extremely challenging task that is out of grasp even for experienced healthcare profession…
Segmentation of Bruch's Membrane in retinal OCT with AMD using anatomical priors and uncertainty quantification
Botond Fazekas, Dmitrii Lachinov, Guilherme Aresta +3
Bruch's membrane (BM) segmentation on optical coherence tomography (OCT) is a pivotal step for the diagnosis and follow-up of age-related macular degeneration (AMD), one of the lea…
Metadata-enhanced contrastive learning from retinal optical coherence tomography images
Robbie Holland, Oliver Leingang, Hrvoje Bogunović +9
Deep learning has potential to automate screening, monitoring and grading of disease in medical images. Pretraining with contrastive learning enables models to extract robust and g…
SD-LayerNet: Semi-supervised retinal layer segmentation in OCT using disentangled representation with anatomical priors
Botond Fazekas, Guilherme Aresta, Dmitrii Lachinov +4
Optical coherence tomography (OCT) is a non-invasive 3D modality widely used in ophthalmology for imaging the retina. Achieving automated, anatomically coherent retinal layer segme…
Deep Dirichlet uncertainty for unsupervised out-of-distribution detection of eye fundus photographs in glaucoma screening
Teresa Araújo, Guilherme Aresta, Hrvoje Bogunovic
The development of automatic tools for early glaucoma diagnosis with color fundus photographs can significantly reduce the impact of this disease. However, current state-of-the-art…
Projective Skip-Connections for Segmentation Along a Subset of Dimensions in Retinal OCT
Dmitrii Lachinov, Philipp Seeboeck, Julia Mai +2
In medical imaging, there are clinically relevant segmentation tasks where the output mask is a projection to a subset of input image dimensions. In this work, we propose a novel c…
U-Net with spatial pyramid pooling for drusen segmentation in optical coherence tomography
Rhona Asgari, Sebastian Waldstein, Ferdinand Schlanitz +3
The presence of drusen is the main hallmark of early/intermediate age-related macular degeneration (AMD). Therefore, automated drusen segmentation is an important step in image-gui…
Modeling Disease Progression In Retinal OCTs With Longitudinal Self-Supervised Learning
Antoine Rivail, Ursula Schmidt-Erfurth, Wolf-Dieter Vogl +5
Longitudinal imaging is capable of capturing the static ana\-to\-mi\-cal structures and the dynamic changes of the morphology resulting from aging or disease progression. Self-supe…
An amplified-target loss approach for photoreceptor layer segmentation in pathological OCT scans
José Ignacio Orlando, Anna Breger, Hrvoje Bogunović +4
Segmenting anatomical structures such as the photoreceptor layer in retinal optical coherence tomography (OCT) scans is challenging in pathological scenarios. Supervised deep learn…
Multiclass segmentation as multitask learning for drusen segmentation in retinal optical coherence tomography
Rhona Asgari, José Ignacio Orlando, Sebastian Waldstein +4
Automated drusen segmentation in retinal optical coherence tomography (OCT) scans is relevant for understanding age-related macular degeneration (AMD) risk and progression. This ta…
Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT
Philipp Seeböck, José Ignacio Orlando, Thomas Schlegl +5
Diagnosis and treatment guidance are aided by detecting relevant biomarkers in medical images. Although supervised deep learning can perform accurate segmentation of pathological a…
Using CycleGANs for effectively reducing image variability across OCT devices and improving retinal fluid segmentation
Philipp Seeböck, David Romo-Bucheli, Sebastian Waldstein +5
Optical coherence tomography (OCT) has become the most important imaging modality in ophthalmology. A substantial amount of research has recently been devoted to the development of…
U2-Net: A Bayesian U-Net model with epistemic uncertainty feedback for photoreceptor layer segmentation in pathological OCT scans
José Ignacio Orlando, Philipp Seeböck, Hrvoje Bogunović +5
In this paper, we introduce a Bayesian deep learning based model for segmenting the photoreceptor layer in pathological OCT scans. Our architecture provides accurate segmentations…
Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data
Philipp Seeböck, Sebastian M. Waldstein, Sophie Klimscha +6
The identification and quantification of markers in medical images is critical for diagnosis, prognosis, and disease management. Supervised machine learning enables the detection a…
Fully Automated Segmentation of Hyperreflective Foci in Optical Coherence Tomography Images
Thomas Schlegl, Hrvoje Bogunovic, Sophie Klimscha +6
The automatic detection of disease related entities in retinal imaging data is relevant for disease- and treatment monitoring. It enables the quantitative assessment of large amoun…
Reproducibility of Retinal Thickness Measurements across Spectral-Domain Optical Coherence Tomography Devices using Iowa Reference Algorithm
Adnan Rashid, Sebastian M. Waldstein, Bianca S. Gerendas +8
PURPOSE: Establishing and obtaining consistent quantitative indices of retinal thickness from a variety of clinically used Spectral-Domain Optical Coherence Tomography scanners. DE…