# From Denoising Training to Test-Time Adaptation: Enhancing Domain Generalization for Medical Image Segmentation

arXiv: https://arxiv.org/abs/2310.20271 · HTML: https://mlsanity.com/paper/2310.20271/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 47 of its 67 references that are in the map.
- First posted on arXiv 31 Oct 2023.

## Builds on (most cited first, 30 of 47)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [Moment Matching for Multi-Source Domain Adaptation](https://mlsanity.com/paper/1812.01754/) (2018)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Temporal Ensembling for Semi-Supervised Learning](https://mlsanity.com/paper/1610.02242/) (2016)
- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021)
- [Taskonomy: Disentangling Task Transfer Learning](https://mlsanity.com/paper/1804.08328/) (2018)
- [The Liver Tumor Segmentation Benchmark (LiTS)](https://mlsanity.com/paper/1901.04056/) (2019)
- [Generalizing to Unseen Domains via Adversarial Data Augmentation](https://mlsanity.com/paper/1805.12018/) (2018)
- [Domain Generalization by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1903.06864/) (2019)
- [Noise2Void - Learning Denoising from Single Noisy Images](https://mlsanity.com/paper/1811.10980/) (2018)
- [A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts](https://mlsanity.com/paper/2303.15361/) (2023)
- [Big Self-Supervised Models Advance Medical Image Classification](https://mlsanity.com/paper/2101.05224/) (2021)
- [CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation](https://mlsanity.com/paper/2001.06535/) (2020)
- [Learning to Learn Single Domain Generalization](https://mlsanity.com/paper/2003.13216/) (2020)
- [Learning to Generate Novel Domains for Domain Generalization](https://mlsanity.com/paper/2007.03304/) (2020)
- [Deep Domain-Adversarial Image Generation for Domain Generalisation](https://mlsanity.com/paper/2003.06054/) (2020)
- [Test-Time Training with Masked Autoencoders](https://mlsanity.com/paper/2209.07522/) (2022)
- [SelfReg: Self-supervised Contrastive Regularization for Domain Generalization](https://mlsanity.com/paper/2104.09841/) (2021)
- [Domain Generalization by Marginal Transfer Learning](https://mlsanity.com/paper/1711.07910/) (2017)
- [Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1607.03516/) (2016)
- [Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization](https://mlsanity.com/paper/2104.05833/) (2021)
- [Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation](https://mlsanity.com/paper/2004.04668/) (2020)
- [Progressive Domain Expansion Network for Single Domain Generalization](https://mlsanity.com/paper/2103.16050/) (2021)
- [Self-supervised learning methods and applications in medical imaging analysis: A survey](https://mlsanity.com/paper/2109.08685/) (2021)
- [Source-Free Domain Adaptive Fundus Image Segmentation with Denoised Pseudo-Labeling](https://mlsanity.com/paper/2109.09735/) (2021)
- [Generalizable Cross-modality Medical Image Segmentation via Style Augmentation and Dual Normalization](https://mlsanity.com/paper/2112.11177/) (2021)

## Built on by (most cited first, 20 of 20)

- [Keeping Medical AI Healthy and Trustworthy: A Review of Detection and Correction Methods for System Degradation](https://mlsanity.com/paper/2506.17442/) (2025)
- [Navigating Distribution Shifts in Medical Image Analysis: A Survey](https://mlsanity.com/paper/2411.05824/) (2024)
- [Progressive Test Time Energy Adaptation for Medical Image Segmentation](https://mlsanity.com/paper/2503.16616/) (2025)
- [Test-time Adaptation for Foundation Medical Segmentation Model without Parametric Updates](https://mlsanity.com/paper/2504.02008/) (2025)
- [A Large Scale Benchmark for Test Time Adaptation Methods in Medical Image Segmentation](https://mlsanity.com/paper/2512.02497/) (2025)
- [Mixture-of-Shape-Experts (MoSE): End-to-End Shape Dictionary Framework to Prompt SAM for Generalizable Medical Segmentation](https://mlsanity.com/paper/2504.09601/) (2025)
- [SAM-aware Test-time Adaptation for Universal Medical Image Segmentation](https://mlsanity.com/paper/2506.05221/) (2025)
- [Test-Time Domain Generalization via Universe Learning: A Multi-Graph Matching Approach for Medical Image Segmentation](https://mlsanity.com/paper/2503.13012/) (2025)
- [Predicting Postoperative Intraocular Lens Dislocation in Cataract Surgery via Deep Learning](https://mlsanity.com/paper/2312.03401/) (2023)
- [Tissue-Contrastive Semi-Masked Autoencoders for Segmentation Pretraining on Chest CT](https://mlsanity.com/paper/2407.08961/) (2024)
- [ProKAN: Progressive Stacking of Kolmogorov-Arnold Networks for Efficient Liver Segmentation](https://mlsanity.com/paper/2412.19713/) (2024)
- [Sample-Aware Test-Time Adaptation for Medical Image-to-Image Translation](https://mlsanity.com/paper/2508.00766/) (2025)
- [HyperTTA: Test-Time Adaptation for Hyperspectral Image Classification under Distribution Shifts](https://mlsanity.com/paper/2509.08436/) (2025)
- [Tumor-anchored deep feature random forests for out-of-distribution detection in lung cancer segmentation](https://mlsanity.com/paper/2512.08216/) (2025)
- [SAMora: Enhancing SAM through Hierarchical Self-Supervised Pre-Training for Medical Images](https://mlsanity.com/paper/2511.08626/) (2025)
- [Boundary-Aware Test-Time Adaptation for Zero-Shot Medical Image Segmentation](https://mlsanity.com/paper/2512.04520/) (2025)
- [ImPartial: Multi-channel Whole-Cell Segmentation using Partial Annotations](https://mlsanity.com/paper/2605.24128/) (2026)
- [Improving PET/CT-Based Whole-Body Lesion Segmentation Using Prediction Uncertainty-Augmented Models](https://mlsanity.com/paper/2606.10115/) (2026)
- [Learning a Sampling-Free Variational DNN Plugin from Tiny Training Sets to Refine OOD Segmentation With Uncertainty Estimation](https://mlsanity.com/paper/2606.15837/) (2026)
- [DistMedVL: Distributional Vision-Language Alignment for Uncertainty-Aware Medical Image Segmentation](https://mlsanity.com/paper/2608.05683/) (2026)

## Abstract (arXiv)

In medical image segmentation, domain generalization poses a significant challenge due to domain shifts caused by variations in data acquisition devices and other factors. These shifts are particularly pronounced in the most common scenario, which involves only single-source domain data due to privacy concerns. To address this, we draw inspiration from the self-supervised learning paradigm that effectively discourages overfitting to the source domain. We propose the Denoising Y-Net (DeY-Net), a novel approach incorporating an auxiliary denoising decoder into the basic U-Net architecture. The auxiliary decoder aims to perform denoising training, augmenting the domain-invariant representation that facilitates domain generalization. Furthermore, this paradigm provides the potential to utilize unlabeled data. Building upon denoising training, we propose Denoising Test Time Adaptation (DeTTA) that further: (i) adapts the model to the target domain in a sample-wise manner, and (ii) adapts to the noise-corrupted input. Extensive experiments conducted on widely-adopted liver segmentation benchmarks demonstrate significant domain generalization improvements over our baseline and state-of-the-art results compared to other methods. Code is available at https://github.com/WenRuxue/DeTTA.