Rethinking Data Augmentation for Single-source Domain Generalization in
Medical Image Segmentation
- URL: http://arxiv.org/abs/2211.14805v1
- Date: Sun, 27 Nov 2022 12:05:33 GMT
- Title: Rethinking Data Augmentation for Single-source Domain Generalization in
Medical Image Segmentation
- Authors: Zixian Su and Kai Yao and Xi Yang and Qiufeng Wang and Jie Sun and
Kaizhu Huang
- Abstract summary: We rethink the data augmentation strategy for single-source domain generalization in medical image segmentation.
Motivated by the class-level representation invariance and style mutability of medical images, we hypothesize that unseen target data can be sampled from a linear combination of $C$ random variables.
We implement such strategy with constrained B$acuterm e$zier transformation on both global and local (i.e. class-level) regions.
As an important contribution, we prove theoretically that our proposed augmentation can lead to an upper bound of the risk generalization on the unseen target domain.
- Score: 19.823497430391413
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Single-source domain generalization (SDG) in medical image segmentation is a
challenging yet essential task as domain shifts are quite common among clinical
image datasets. Previous attempts most conduct global-only/random augmentation.
Their augmented samples are usually insufficient in diversity and
informativeness, thus failing to cover the possible target domain distribution.
In this paper, we rethink the data augmentation strategy for SDG in medical
image segmentation. Motivated by the class-level representation invariance and
style mutability of medical images, we hypothesize that unseen target data can
be sampled from a linear combination of $C$ (the class number) random
variables, where each variable follows a location-scale distribution at the
class level. Accordingly, data augmented can be readily made by sampling the
random variables through a general form. On the empirical front, we implement
such strategy with constrained B$\acute{\rm e}$zier transformation on both
global and local (i.e. class-level) regions, which can largely increase the
augmentation diversity. A Saliency-balancing Fusion mechanism is further
proposed to enrich the informativeness by engaging the gradient information,
guiding augmentation with proper orientation and magnitude. As an important
contribution, we prove theoretically that our proposed augmentation can lead to
an upper bound of the generalization risk on the unseen target domain, thus
confirming our hypothesis. Combining the two strategies, our Saliency-balancing
Location-scale Augmentation (SLAug) exceeds the state-of-the-art works by a
large margin in two challenging SDG tasks. Code is available at
https://github.com/Kaiseem/SLAug .
Related papers
- FIESTA: Fourier-Based Semantic Augmentation with Uncertainty Guidance for Enhanced Domain Generalizability in Medical Image Segmentation [10.351755243183383]
Single-source domain generalization (SDG) in medical image segmentation (MIS) aims to generalize a model using data from only one source domain to segment data from an unseen target domain.
Existing methods often fail to fully consider the details and uncertain areas prevalent in MIS, leading to mis-segmentation.
This paper proposes a Fourier-based semantic augmentation method called FIESTA using uncertainty guidance to enhance the fundamental goals of MIS.
arXiv Detail & Related papers (2024-06-20T13:37:29Z) - Additional Look into GAN-based Augmentation for Deep Learning COVID-19
Image Classification [57.1795052451257]
We study the dependence of the GAN-based augmentation performance on dataset size with a focus on small samples.
We train StyleGAN2-ADA with both sets and then, after validating the quality of generated images, we use trained GANs as one of the augmentations approaches in multi-class classification problems.
The GAN-based augmentation approach is found to be comparable with classical augmentation in the case of medium and large datasets but underperforms in the case of smaller datasets.
arXiv Detail & Related papers (2024-01-26T08:28:13Z) - Curriculum-Based Augmented Fourier Domain Adaptation for Robust Medical
Image Segmentation [18.830738606514736]
This work proposes the Curriculum-based Augmented Fourier Domain Adaptation (Curri-AFDA) for robust medical image segmentation.
In particular, our curriculum learning strategy is based on the causal relationship of a model under different levels of data shift.
Experiments on two segmentation tasks of Retina and Nuclei collected from multiple sites and scanners suggest that our proposed method yields superior adaptation and generalization performance.
arXiv Detail & Related papers (2023-06-06T08:56:58Z) - Augmentation-based Domain Generalization for Semantic Segmentation [2.179313476241343]
Unsupervised Domain Adaptation (UDA) and domain generalization (DG) aim to tackle the lack of generalization of Deep Neural Networks (DNNs) towards unseen domains.
We study the in- and out-of-domain generalization capabilities of simple, rule-based image augmentations like blur, noise, color jitter and many more.
Our experiments confirm the common scientific standard that combination of multiple different augmentations out-performs single augmentations.
arXiv Detail & Related papers (2023-04-24T14:26:53Z) - Domain Generalization with Adversarial Intensity Attack for Medical
Image Segmentation [27.49427483473792]
In real-world scenarios, it is common for models to encounter data from new and different domains to which they were not exposed to during training.
domain generalization (DG) is a promising direction as it enables models to handle data from previously unseen domains.
We introduce a novel DG method called Adversarial Intensity Attack (AdverIN), which leverages adversarial training to generate training data with an infinite number of styles.
arXiv Detail & Related papers (2023-04-05T19:40:51Z) - When Neural Networks Fail to Generalize? A Model Sensitivity Perspective [82.36758565781153]
Domain generalization (DG) aims to train a model to perform well in unseen domains under different distributions.
This paper considers a more realistic yet more challenging scenario, namely Single Domain Generalization (Single-DG)
We empirically ascertain a property of a model that correlates strongly with its generalization that we coin as "model sensitivity"
We propose a novel strategy of Spectral Adversarial Data Augmentation (SADA) to generate augmented images targeted at the highly sensitive frequencies.
arXiv Detail & Related papers (2022-12-01T20:15:15Z) - FIXED: Frustratingly Easy Domain Generalization with Mixup [53.782029033068675]
Domain generalization (DG) aims to learn a generalizable model from multiple training domains such that it can perform well on unseen target domains.
A popular strategy is to augment training data to benefit generalization through methods such as Mixupcitezhang 2018mixup.
We propose a simple yet effective enhancement for Mixup-based DG, namely domain-invariant Feature mIXup (FIX)
Our approach significantly outperforms nine state-of-the-art related methods, beating the best performing baseline by 6.5% on average in terms of test accuracy.
arXiv Detail & Related papers (2022-11-07T09:38:34Z) - AADG: Automatic Augmentation for Domain Generalization on Retinal Image
Segmentation [1.0452185327816181]
We propose a data manipulation based domain generalization method, called Automated Augmentation for Domain Generalization (AADG)
Our AADG framework can effectively sample data augmentation policies that generate novel domains.
Our proposed AADG exhibits state-of-the-art generalization performance and outperforms existing approaches.
arXiv Detail & Related papers (2022-07-27T02:26:01Z) - Cross-Modality Brain Tumor Segmentation via Bidirectional
Global-to-Local Unsupervised Domain Adaptation [61.01704175938995]
In this paper, we propose a novel Bidirectional Global-to-Local (BiGL) adaptation framework under a UDA scheme.
Specifically, a bidirectional image synthesis and segmentation module is proposed to segment the brain tumor.
The proposed method outperforms several state-of-the-art unsupervised domain adaptation methods by a large margin.
arXiv Detail & Related papers (2021-05-17T10:11:45Z) - Semi-Supervised Domain Adaptation with Prototypical Alignment and
Consistency Learning [86.6929930921905]
This paper studies how much it can help address domain shifts if we further have a few target samples labeled.
To explore the full potential of landmarks, we incorporate a prototypical alignment (PA) module which calculates a target prototype for each class from the landmarks.
Specifically, we severely perturb the labeled images, making PA non-trivial to achieve and thus promoting model generalizability.
arXiv Detail & Related papers (2021-04-19T08:46:08Z) - Alleviating Semantic-level Shift: A Semi-supervised Domain Adaptation
Method for Semantic Segmentation [97.8552697905657]
A key challenge of this task is how to alleviate the data distribution discrepancy between the source and target domains.
We propose Alleviating Semantic-level Shift (ASS), which can successfully promote the distribution consistency from both global and local views.
We apply our ASS to two domain adaptation tasks, from GTA5 to Cityscapes and from Synthia to Cityscapes.
arXiv Detail & Related papers (2020-04-02T03:25:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.