Fig. 4 | Scientific Reports

Fig. 4

From: Multiple instance learning using pathology foundation models effectively predicts kidney disease diagnosis and clinical classification

Fig. 4

Attention heatmaps and top nine high-attention patches. HC (a), AIN (b), and DKD (c) slides in the UT dataset were used to visualize how each model built on seven encoders (ResNet50, UNI, UNI2-h, Prov-Gigapath, Phikon, Virchow, and Virchow2) extracted features from image patches. Attention scores for the true label (HC, AIN, or DKD) were used to visualize attention heatmaps with Turbo colormap, where warmer colors (yellow to red) represent higher attention scores, and cooler colors (green to blue) indicate lower attention scores. The top nine patches with the highest attention scores were identified. Scale bar = 500 μm. HC healthy control, AIN acute interstitial nephritis, DKD diabetic kidney disease.

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