Fig. 1: The flowchart illustrating the study design.
From: Clinically applicable optimized periprosthetic joint infection diagnosis via AI based pathology
Purple arrows indicate input, black arrows indicate output, flames represent trainable components, and locks denote testing-only components. a Data processing: WSI datasets were segmented into 600 × 600-pixel patches and divided for DINO v2 training, testing, and additional training. b Self-supervised model and augmentation: b1 pathological images trained the DINO v2 model. b2 The DINO v2 backbone extracted features, with the fully connected layer trained. b3 Test data were reserved for testing, with additional data used for self-supervised tasks. b4 Self-supervised model testing results. c Multi-model training: c1 expert-reviewed data trained various models. c2 and c3 Each model was optimized, tested, and compared.
