26 citations · 95 across the 24 of their papers we have counts for
6 papers · 1 filter
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…
Data-centric AI approach to improve optic nerve head segmentation and localization in OCT en face images
Thomas Schlegl, Heiko Stino, Michael Niederleithner +5
The automatic detection and localization of anatomical features in retinal imaging data are relevant for many aspects. In this work, we follow a data-centric approach to optimize c…
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…
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…