31 datascEYEnce! Computer Vision News To account for this, his research focuses on using deep learning for automatic temporal biomarker discovery. In other words, clustering disease progression trajectories in a pre-trained feature space. The choice of a self-supervised approach, specifically contrastive learning, was made in order to identify trajectories, or more precisely sub-trajectories. Contrastive learning methods have shown their capability to autonomously learn disease-related features (including previously unknown biomarkers) without the need for clinical guidance. For the setup, a ResNet-50 backbone was trained on costeffective yet informative OCT scans. The self-supervised loss function BYOL contrastive loss makes it possible to train only on positive samples. The decision to use this
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