Fig. 9: Architecture of PJI self-supervised learning model.
From: Clinically applicable optimized periprosthetic joint infection diagnosis via AI based pathology
A teacher–student model structure with different data augmentations is used, where the teacher model is updated using the student model’s exponential moving average (EMA). Both networks feature a ViT backbone, a projection head, and use temperature softmax. DINO v2 introduces patch tokens and masking, with the student network projecting masked views and the teacher network projecting unmasked views. The training objective of iBOT is defined based on this setup. DINO v2 model learns representations of unlabeled pathological sections through a self-supervised learning loss function.
