# Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation

arXiv: https://arxiv.org/abs/1909.11524 · HTML: https://mlsanity.com/paper/1909.11524/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2019.

## Built on by (most cited first, 4 of 4)

- [Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives](https://mlsanity.com/paper/2308.01265/) (2023)
- [Semi-supervised Pathological Image Segmentation via Cross Distillation of Multiple Attentions](https://mlsanity.com/paper/2305.18830/) (2023)
- [Domain Adaptive Cascade R-CNN for MItosis DOmain Generalization (MIDOG) Challenge](https://mlsanity.com/paper/2109.00965/) (2021)
- [A Foundation Model for DAS Signal Recognition and Visual Prompt Tuning of the Pre-trained Model for Downstream Tasks](https://mlsanity.com/paper/2508.04316/) (2025)

## Abstract (arXiv)

Supervised semantic segmentation normally assumes the test data being in a similar data domain as the training data. However, in practice, the domain mismatch between the training and unseen data could lead to a significant performance drop. Obtaining accurate pixel-wise label for images in different domains is tedious and labor intensive, especially for histopathology images. In this paper, we propose a dual adaptive pyramid network (DAPNet) for histopathological gland segmentation adapting from one stain domain to another. We tackle the domain adaptation problem on two levels: 1) the image-level considers the differences of image color and style; 2) the feature-level addresses the spatial inconsistency between two domains. The two components are implemented as domain classifiers with adversarial training. We evaluate our new approach using two gland segmentation datasets with H&E and DAB-H stains respectively. The extensive experiments and ablation study demonstrate the effectiveness of our approach on the domain adaptive segmentation task. We show that the proposed approach performs favorably against other state-of-the-art methods.