# Domain and Content Adaptive Convolution based Multi-Source Domain Generalization for Medical Image Segmentation

arXiv: https://arxiv.org/abs/2109.05676 · HTML: https://mlsanity.com/paper/2109.05676/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 55 references that are in the map.
- First posted on arXiv 13 Sep 2021.

## Builds on (most cited first, 26 of 26)

- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [FDA: Fourier Domain Adaptation for Semantic Segmentation](https://mlsanity.com/paper/2004.05498/) (2020)
- [Dynamic Neural Networks: A Survey](https://mlsanity.com/paper/2102.04906/) (2021)
- [Conditional Convolutions for Instance Segmentation](https://mlsanity.com/paper/2003.05664/) (2020)
- [FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space](https://mlsanity.com/paper/2103.06030/) (2021)
- [Episodic Training for Domain Generalization](https://mlsanity.com/paper/1902.00113/) (2019)
- [REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs](https://mlsanity.com/paper/1910.03667/) (2019)
- [Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation](https://mlsanity.com/paper/2002.02255/) (2020)
- [Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains](https://mlsanity.com/paper/2007.02035/) (2020)
- [Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization](https://mlsanity.com/paper/2009.12829/) (2020)
- [MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Data](https://mlsanity.com/paper/2002.03366/) (2020)
- [Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation](https://mlsanity.com/paper/2004.04668/) (2020)
- [DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasets](https://mlsanity.com/paper/2011.10217/) (2020)
- [Adversarially Adaptive Normalization for Single Domain Generalization](https://mlsanity.com/paper/2106.01899/) (2021)
- [Hierarchical Dynamic Filtering Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2007.06227/) (2020)
- [DoFE: Domain-oriented Feature Embedding for Generalizable Fundus Image Segmentation on Unseen Datasets](https://mlsanity.com/paper/2010.06208/) (2020)
- [Decoupled Dynamic Filter Networks](https://mlsanity.com/paper/2104.14107/) (2021)
- [Boundary and Entropy-driven Adversarial Learning for Fundus Image Segmentation](https://mlsanity.com/paper/1906.11143/) (2019)
- [Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image Segmentation](https://mlsanity.com/paper/2106.13292/) (2021)
- [Domain Generalization on Medical Imaging Classification using Episodic Training with Task Augmentation](https://mlsanity.com/paper/2106.06908/) (2021)
- [Cooperative Training and Latent Space Data Augmentation for Robust Medical Image Segmentation](https://mlsanity.com/paper/2107.01079/) (2021)
- [Robust White Matter Hyperintensity Segmentation on Unseen Domain](https://mlsanity.com/paper/2102.06650/) (2021)
- [Noise-Powered Disentangled Representation for Unsupervised Speckle Reduction of Optical Coherence Tomography Images](https://mlsanity.com/paper/2007.03446/) (2020)
- [Towards Unbiased COVID-19 Lesion Localisation and Segmentation via Weakly Supervised Learning](https://mlsanity.com/paper/2103.00780/) (2021)

## Built on by (most cited first, 30 of 30)

- [A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts](https://mlsanity.com/paper/2303.15361/) (2023)
- [Devil is in Channels: Contrastive Single Domain Generalization for Medical Image Segmentation](https://mlsanity.com/paper/2306.05254/) (2023)
- [CDDSA: Contrastive Domain Disentanglement and Style Augmentation for Generalizable Medical Image Segmentation](https://mlsanity.com/paper/2211.12081/) (2022)
- [Treasure in Distribution: A Domain Randomization based Multi-Source Domain Generalization for 2D Medical Image Segmentation](https://mlsanity.com/paper/2305.19949/) (2023)
- [MoreStyle: Relax Low-frequency Constraint of Fourier-based Image Reconstruction in Generalizable Medical Image Segmentation](https://mlsanity.com/paper/2403.11689/) (2024)
- [Medical Image Segmentation via Single-Source Domain Generalization with Random Amplitude Spectrum Synthesis](https://mlsanity.com/paper/2409.04768/) (2024)
- [Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation](https://mlsanity.com/paper/2504.17515/) (2025)
- [The Devil is in the Statistics: Mitigating and Exploiting Statistics Difference for Generalizable Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2407.11356/) (2024)
- [NuSegDG: Integration of Heterogeneous Space and Gaussian Kernel for Domain-Generalized Nuclei Segmentation](https://mlsanity.com/paper/2408.11787/) (2024)
- [CoSAM: Self-Correcting SAM for Domain Generalization in 2D Medical Image Segmentation](https://mlsanity.com/paper/2411.10136/) (2024)
- [Exploring Generalizable Distillation for Efficient Medical Image Segmentation](https://mlsanity.com/paper/2207.12995/) (2022)
- [FIESTA: Fourier-Based Semantic Augmentation with Uncertainty Guidance for Enhanced Domain Generalizability in Medical Image Segmentation](https://mlsanity.com/paper/2406.14308/) (2024)
- [A Dual-Task Synergy-Driven Generalization Framework for Pancreatic Cancer Segmentation in CT Scans](https://mlsanity.com/paper/2505.01644/) (2025)
- [Progressive Dual Priori Network for Generalized Breast Tumor Segmentation](https://mlsanity.com/paper/2310.13574/) (2023)
- [Parameter Exchange for Robust Dynamic Domain Generalization](https://mlsanity.com/paper/2311.13928/) (2023)
- [DG-TTA: Out-of-domain Medical Image Segmentation through Augmentation and Descriptor-driven Domain Generalization and Test-Time Adaptation](https://mlsanity.com/paper/2312.06275/) (2023)
- [Leveraging SAM for Single-Source Domain Generalization in Medical Image Segmentation](https://mlsanity.com/paper/2401.02076/) (2024)
- [Neuroverse3D: Developing In-Context Learning Universal Model for Neuroimaging in 3D](https://mlsanity.com/paper/2503.02410/) (2025)
- [LangDAug: Langevin Data Augmentation for Multi-Source Domain Generalization in Medical Image Segmentation](https://mlsanity.com/paper/2505.19659/) (2025)
- [SRA-Seg: Synthetic to Real Alignment for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2602.02944/) (2026)
- [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)
- [Domain and Content Adaptive Convolutions for Cross-Domain Adenocarcinoma Segmentation](https://mlsanity.com/paper/2409.09797/) (2024)
- [Structure-Aware Stylized Image Synthesis for Robust Medical Image Segmentation](https://mlsanity.com/paper/2412.04296/) (2024)
- [Advancing Cross-Organ Domain Generalization with Test-Time Style Transfer and Diversity Enhancement](https://mlsanity.com/paper/2503.18567/) (2025)
- [AdaViT: Adaptive Vision Transformer for Flexible Pretrain and Finetune with Variable 3D Medical Image Modalities](https://mlsanity.com/paper/2504.03589/) (2025)
- [VIViT: Variable-Input Vision Transformer Framework for 3D MR Image Segmentation](https://mlsanity.com/paper/2505.08693/) (2025)
- [SpinalSAM-R1: A Vision-Language Multimodal Interactive System for Spine CT Segmentation](https://mlsanity.com/paper/2511.00095/) (2025)
- [SemiTooth: a Generalizable Semi-supervised Framework for Multi-Source Tooth Segmentation](https://mlsanity.com/paper/2603.11616/) (2026)
- [Why Invariance is Not Enough for Biomedical Domain Generalization and How to Fix It](https://mlsanity.com/paper/2604.02564/) (2026)
- [CoDG-Net: Structure-Guided Style Diffusion and Collaborative Learning to Mitigate Catastrophic Forgetting in Medical Image Domain Generalization](https://mlsanity.com/paper/2610.05053/) (2026)

## Abstract (arXiv)

The domain gap caused mainly by variable medical image quality renders a major obstacle on the path between training a segmentation model in the lab and applying the trained model to unseen clinical data. To address this issue, domain generalization methods have been proposed, which however usually use static convolutions and are less flexible. In this paper, we propose a multi-source domain generalization model based on the domain and content adaptive convolution (DCAC) for the segmentation of medical images across different modalities. Specifically, we design the domain adaptive convolution (DAC) module and content adaptive convolution (CAC) module and incorporate both into an encoder-decoder backbone. In the DAC module, a dynamic convolutional head is conditioned on the predicted domain code of the input to make our model adapt to the unseen target domain. In the CAC module, a dynamic convolutional head is conditioned on the global image features to make our model adapt to the test image. We evaluated the DCAC model against the baseline and four state-of-the-art domain generalization methods on the prostate segmentation, COVID-19 lesion segmentation, and optic cup/optic disc segmentation tasks. Our results not only indicate that the proposed DCAC model outperforms all competing methods on each segmentation task but also demonstrate the effectiveness of the DAC and CAC modules. Code is available at urlhttps://git.io/DCAC.