8 citations · 12 across the 9 of their papers we have counts for
9 papers
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…
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…
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…
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…
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…
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…
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…
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…