# Domain Composition and Attention for Unseen-Domain Generalizable Medical Image Segmentation

arXiv: https://arxiv.org/abs/2109.08852 · HTML: https://mlsanity.com/paper/2109.08852/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 23 references that are in the map.
- First posted on arXiv 18 Sep 2021.

## Builds on (most cited first, 17 of 17)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Learning to Generalize: Meta-Learning for Domain Generalization](https://mlsanity.com/paper/1710.03463/) (2017)
- [Domain Generalization via Model-Agnostic Learning of Semantic Features](https://mlsanity.com/paper/1910.13580/) (2019)
- [Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1708.08987/) (2017)
- [Attention to Scale: Scale-aware Semantic Image Segmentation](https://mlsanity.com/paper/1511.03339/) (2015)
- [Unsupervised domain adaptation in brain lesion segmentation with adversarial networks](https://mlsanity.com/paper/1612.08894/) (2016)
- [Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains](https://mlsanity.com/paper/2007.02035/) (2020)
- [MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Data](https://mlsanity.com/paper/2002.03366/) (2020)
- [Unsupervised domain adaptation for medical imaging segmentation with self-ensembling](https://mlsanity.com/paper/1811.06042/) (2018)
- [Towards Universal Object Detection by Domain Attention](https://mlsanity.com/paper/1904.04402/) (2019)
- [CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation](https://mlsanity.com/paper/2009.10549/) (2020)
- [DoFE: Domain-oriented Feature Embedding for Generalizable Fundus Image Segmentation on Unseen Datasets](https://mlsanity.com/paper/2010.06208/) (2020)
- [Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations with Adversarial Loss](https://mlsanity.com/paper/1804.10916/) (2018)
- [Semantic-Aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X-ray Segmentation](https://mlsanity.com/paper/1806.00600/) (2018)

## Built on by (most cited first, 16 of 16)

- [Causality-inspired Single-source Domain Generalization for Medical Image Segmentation](https://mlsanity.com/paper/2111.12525/) (2021)
- [UPL-SFDA: Uncertainty-aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation](https://mlsanity.com/paper/2309.10244/) (2023)
- [CDDSA: Contrastive Domain Disentanglement and Style Augmentation for Generalizable Medical Image Segmentation](https://mlsanity.com/paper/2211.12081/) (2022)
- [vMFNet: Compositionality Meets Domain-generalised Segmentation](https://mlsanity.com/paper/2206.14538/) (2022)
- [Feasibility and benefits of joint learning from MRI databases with different brain diseases and modalities for segmentation](https://mlsanity.com/paper/2405.18511/) (2024)
- [Segmentation Consistency Training: Out-of-Distribution Generalization for Medical Image Segmentation](https://mlsanity.com/paper/2205.15428/) (2022)
- [Compositionally Equivariant Representation Learning](https://mlsanity.com/paper/2306.07783/) (2023)
- [A Dual-Task Synergy-Driven Generalization Framework for Pancreatic Cancer Segmentation in CT Scans](https://mlsanity.com/paper/2505.01644/) (2025)
- [Contrastive Domain Disentanglement for Generalizable Medical Image Segmentation](https://mlsanity.com/paper/2205.06551/) (2022)
- [M3DA: Benchmark for Unsupervised Domain Adaptation in 3D Medical Image Segmentation](https://mlsanity.com/paper/2502.17029/) (2025)
- [LangDAug: Langevin Data Augmentation for Multi-Source Domain Generalization in Medical Image Segmentation](https://mlsanity.com/paper/2505.19659/) (2025)
- [Tumor-anchored deep feature random forests for out-of-distribution detection in lung cancer segmentation](https://mlsanity.com/paper/2512.08216/) (2025)
- [MLA-BIN: Model-level Attention and Batch-instance Style Normalization for Domain Generalization of Federated Learning on Medical Image Segmentation](https://mlsanity.com/paper/2306.17008/) (2023)
- [A Survey on Cell Nuclei Instance Segmentation and Classification: Leveraging Context and Attention](https://mlsanity.com/paper/2407.18673/) (2024)
- [CRISP: Rank-Guided Iterative Squeezing for Robust Medical Image Segmentation under Domain Shift](https://mlsanity.com/paper/2604.05409/) (2026)
- [CRISP: Constrained Refinement via Iterative Squeezing Process for Robust Medical Image Segmentation under Domain Shift](https://mlsanity.com/paper/2607.15231/) (2026)

## Abstract (arXiv)

Domain generalizable model is attracting increasing attention in medical image analysis since data is commonly acquired from different institutes with various imaging protocols and scanners. To tackle this challenging domain generalization problem, we propose a Domain Composition and Attention-based network (DCA-Net) to improve the ability of domain representation and generalization. First, we present a domain composition method that represents one certain domain by a linear combination of a set of basis representations (i.e., a representation bank). Second, a novel plug-and-play parallel domain preceptor is proposed to learn these basis representations and we introduce a divergence constraint function to encourage the basis representations to be as divergent as possible. Then, a domain attention module is proposed to learn the linear combination coefficients of the basis representations. The result of linear combination is used to calibrate the feature maps of an input image, which enables the model to generalize to different and even unseen domains. We validate our method on public prostate MRI dataset acquired from six different institutions with apparent domain shift. Experimental results show that our proposed model can generalize well on different and even unseen domains and it outperforms state-of-the-art methods on the multi-domain prostate segmentation task.