80 citations · 148 across the 24 of their papers we have counts for
5 papers · 1 filter
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…