5 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…
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…
MIRAGE: Multimodal foundation model and benchmark for comprehensive retinal OCT image analysis
José Morano, Botond Fazekas, Emese Sükei +7
Artificial intelligence (AI) has become a fundamental tool for assisting clinicians in analyzing ophthalmic images, such as optical coherence tomography (OCT). However, developing…
Automatic detection and prediction of nAMD activity change in retinal OCT using Siamese networks and Wasserstein Distance for ordinality
Taha Emre, Teresa Araújo, Marzieh Oghbaie +3
Neovascular age-related macular degeneration (nAMD) is a leading cause of vision loss among older adults, where disease activity detection and progression prediction are critical f…
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…