Fig. 8: Architecture of the PJI weakly supervised learning model.
From: Clinically applicable optimized periprosthetic joint infection diagnosis via AI based pathology
The model has three components: cMIL, Label Enrichment, and Segmentation. cMIL performs fine-grained segmentation, Label Enrichment extends image data, and Segmentation re-segments the image. Using CAMEL2, we transform coarse-grained labeling into a fine-grained classification task, generating pseudo-labels and applying Multi-Instance Learning (MIL) to create an instance-level dataset. Terahertz images are divided into grids that share label information with the entire image. Positive and negative samples form patch-level pairs, with images expanded to 2048 × 2048 pixels, segmented into 256 × 256 grid instances, and processed with softmax to obtain probabilities. In negative samples, instances inherit a label of 0, while in positive samples, the top K% of confident instances are selected as positive. Cross-entropy loss updates the model during backpropagation.
