ACS-SegNet: An Attention-Based CNN-SegFormer Segmentation Network for Tissue Segmentation in Histopathology

This repository contains the implementation details of the ACS-SEGNET model.

Citation

Our paper preprint is available on arXiv: https://arxiv.org/abs/2510.20754

BibTex entry:

@article{torbati2025acs,
  title={ACS-SegNet: An Attention-Based CNN-SegFormer Segmentation Network for Tissue Segmentation in Histopathology},
  author={Torbati, Nima and Meshcheryakova, Anastasia and Woitek, Ramona and Mechtcheriakova, Diana and Mahbod, Amirreza},
  journal={arXiv preprint arXiv:2510.20754},
  year={2025}
}

Results

Table 1. Segmentation results on the GCPS dataset

Method	μIoU (%)	μDice (%)
DGAUNet [15]	75.95 ± 0.20	86.33 ± 0.13
SegFormer [13]	70.90 ± 0.38	82.97 ± 0.26
ResNetUNet [17]	75.65 ± 0.08	86.13 ± 0.05
TransUNet [6]	74.84 ± 0.10	85.61 ± 0.09
CS-SegNet	76.68 ± 0.15	86.80 ± 0.06
ACS-SegNet	76.79 ± 0.14	86.87 ± 0.09

Table 2. Segmentation results on the PUMA dataset

Method	μIoU (%)	μDice (%)
DGAUNet [15]	44.35 ± 1.76	53.69 ± 1.91
SegFormer [13]	46.78 ± 2.58	58.25 ± 2.01
ResNetUNet [17]	58.42 ± 1.98	71.58 ± 1.21
TransUNet [6]	62.43 ± 2.47	74.63 ± 1.91
CS-SegNet	63.67 ± 1.23	75.55 ± 1.71
ACS-SegNet	64.93 ± 2.28	76.60 ± 1.36

Example Predictions

Acknowledgements

This project has been conducted through a joint WWTF-funded project (Grant ID: 10.47379/LS23006) between the Medical University of Vienna and Danube Private University.

Academic Research Use: This work is provided "as is", without warranty of any kind.