Research Article
TSHVNet: Simultaneous Nuclear Instance Segmentation and Classification in Histopathological Images Based on Multiattention Mechanisms
Table 5
_scores of various nuclear categories on different combinations of SimAM modules.
| Method | Dataset metrics | CoNSeP | PanNuke | | | | | | | | | | | |
| HoVer-Net | 0.738 | 0.618 | 0.564 | 0.532 | 0.348 | 0.790 | 0.465 | 0.413 | 0.153 | 0.463 | 0.591 | HoVer-Net+Res | 0.761 | 0.655 | 0.622 | 0.579 | 0.393 | 0.809 | 0.495 | 0.440 | 0.169 | 0.470 | 0.599 | HoVer-Net+Des | 0.760 | 0.666 | 0.615 | 0.570 | 0.360 | 0.806 | 0.480 | 0.445 | 0.168 | 0.471 | 0.563 | HoVer-Net+Res+Des | 0.763 | 0.659 | 0.601 | 0.562 | 0.356 | 0.809 | 0.502 | 0.445 | 0.168 | 0.470 | 0.591 | TSHVNet (Res) | 0.757 | 0.647 | 0.598 | 0.553 | 0.362 | 0.815 | 0.528 | 0.455 | 0.176 | 0.530 | 0.620 | TSHVNet (Des) | 0.759 | 0.650 | 0.600 | 0.560 | 0.360 | 0.816 | 0.527 | 0.454 | 0.177 | 0.532 | 0.621 | TSHVNet | 0.763 | 0.669 | 0.621 | 0.583 | 0.358 | 0.820 | 0.531 | 0.460 | 0.179 | 0.536 | 0.623 |
|
|