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 |