Fig. 7: Architecture of PJI supervised learning model.
From: Clinically applicable optimized periprosthetic joint infection diagnosis via AI based pathology
Using EfficientNet v2-S as the backbone, the model begins with a convolutional layer (conv 3 × 3) with a stride of 2, followed by a series of Fused-MBConv and MBConv blocks, where strides vary between 1 and 2. Some blocks include SE (Squeeze-and-Excitation) ratios. The network concludes with a conv 1 × 1 layer, followed by pooling and a fully connected layer, resulting in an output of 1280 channels. The model is implemented in TensorFlow using the Adam optimizer, and its weights are compared on the validation set every 100 steps.
