Fig. 4

Workflow of data preprocessing and model architecture. (A) The histological slide image, that is, whole slide image (WSI) is digitized, segmented, and tessellated into 224 × 224 patches. (B) ViT model pipeline: Patch image is linearly projected into flattened patches, followed by feature extraction via a transformer layer with multi-head attention. Predictions for various tissue classes are performed using a multi-layer perceptron (MLP). (C) EfficientNet model pipeline: The input image undergoes initial feature extraction via a convolution layer, followed by deep feature extraction using MBConv blocks. Extracted features are then processed through global average pooling, a flatten layer, a dropout layer, and a fully connected (FC) layer for the prediction of various tissue classes.