Table 2 Macro F-score averaged over each tissue type for the SA-HCNN with PTO tiling, the SA-HCNN with Center tiling, the current best baseline CNN (EfficientNetV2) with PTO tiling, SegFormer with center tiling, and Segformer with PTO tiling. Results show that the SA-HCNN approach significantly outperforms the baseline EfficientNetV2 and image segmentation approach, and the PTO tiling provides a small additional performance improvement over Center tiling.

From: Scalable deep learning artificial intelligence histopathology slide analysis and validation

Macro F-Score

Testis

Prostate

Female Kidney

Male Kidney

Ovary

SA-HCNN +

PTO Tiling Method

0.9970

0.9848

0.9861

0.9881

0.9942

SA-HCNN +

Center Tiling Method

0.9847

0.9062

0.9779

0.9894

0.9095

Baseline EfficientNetV2 +

PTO Tiling Method

0.9360

0.7789

0.8453

0.8519

0.8297

SegFormer +

Center Tiling Method

0.4148

0.2477

0.5235

0.3548

0.2769

SegFormer +

PTO Tiling Method

0.3265

0.2489

0.2394

0.3028

0.2633