151 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University College LondonGB5 papers
- Moorfields Eye Hospital NHS Foundation TrustGB4 papers
- NIHR Moorfields Biomedical Research CentreGB3 papers
- Imperial College LondonGB2 papers
- Institute of Molecular and Clinical Ophthalmology BaselCH2 papers
- Medical University of ViennaAT2 papers
- TUM KlinikumDE2 papers
- University of BaselCH2 papers
- University of SouthamptonGB2 papers
- University of WashingtonUS2 papers
- Amsterdam University Medical CentersNL1 paper
- Centre for Eye Research AustraliaAU1 paper
5 papers
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…
A deep learning framework for the detection and quantification of drusen and reticular pseudodrusen on optical coherence tomography
Roy Schwartz, Hagar Khalid, Sandra Liakopoulos +12
Purpose - To develop and validate a deep learning (DL) framework for the detection and quantification of drusen and reticular pseudodrusen (RPD) on optical coherence tomography sca…
Learning to Address Intra-segment Misclassification in Retinal Imaging
Yukun Zhou, Moucheng Xu, Yipeng Hu +4
Accurate multi-class segmentation is a long-standing challenge in medical imaging, especially in scenarios where classes share strong similarity. Segmenting retinal blood vessels i…
Forecasting Future Humphrey Visual Fields Using Deep Learning
Joanne C. Wen, Cecilia S. Lee, Pearse A. Keane +5
Purpose: To determine if deep learning networks could be trained to forecast a future 24-2 Humphrey Visual Field (HVF). Participants: All patients who obtained a HVF 24-2 at the Un…