Augmentation based unsupervised domain adaptation
- URL: http://arxiv.org/abs/2202.11486v1
- Date: Wed, 23 Feb 2022 13:06:07 GMT
- Title: Augmentation based unsupervised domain adaptation
- Authors: Mauricio Orbes-Arteaga, Thomas Varsavsky, Lauge Sorensen, Mads
Nielsen, Akshay Pai, Sebastien Ourselin, Marc Modat, and M Jorge Cardoso
- Abstract summary: Deep learning models trained on small and unrepresentative data tend to outperform when deployed in data that differs from the one used for training.
Our approach takes advantage of the properties of adversarial domain adaptation and consistency training to achieve more robust adaptation.
- Score: 2.304713283039168
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The insertion of deep learning in medical image analysis had lead to the
development of state-of-the art strategies in several applications such a
disease classification, as well as abnormality detection and segmentation.
However, even the most advanced methods require a huge and diverse amount of
data to generalize. Because in realistic clinical scenarios, data acquisition
and annotation is expensive, deep learning models trained on small and
unrepresentative data tend to outperform when deployed in data that differs
from the one used for training (e.g data from different scanners). In this
work, we proposed a domain adaptation methodology to alleviate this problem in
segmentation models. Our approach takes advantage of the properties of
adversarial domain adaptation and consistency training to achieve more robust
adaptation. Using two datasets with white matter hyperintensities (WMH)
annotations, we demonstrated that the proposed method improves model
generalization even in corner cases where individual strategies tend to fail.
Related papers
- Weakly supervised deep learning model with size constraint for prostate cancer detection in multiparametric MRI and generalization to unseen domains [0.90668179713299]
We show that the model achieves on-par performance with strong fully supervised baseline models.
We also observe a performance decrease for both fully supervised and weakly supervised models when tested on unseen data domains.
arXiv Detail & Related papers (2024-11-04T12:24:33Z) - DG-TTA: Out-of-domain medical image segmentation through Domain Generalization and Test-Time Adaptation [43.842694540544194]
We propose to combine domain generalization and test-time adaptation to create a highly effective approach for reusing pre-trained models in unseen target domains.
We demonstrate that our method, combined with pre-trained whole-body CT models, can effectively segment MR images with high accuracy.
arXiv Detail & Related papers (2023-12-11T10:26:21Z) - DGM-DR: Domain Generalization with Mutual Information Regularized
Diabetic Retinopathy Classification [40.35834579068518]
Domain shift between training and testing data presents a significant challenge for training general deep learning models.
We introduce a DG method that re-establishes the model objective function as a pretrained model to the medical imaging field.
Our proposed method consistently outperforms the previous state-of-the-art by a margin of 5.25% in average accuracy and a lower standard deviation.
arXiv Detail & Related papers (2023-09-18T11:17:13Z) - Data Augmentation-Based Unsupervised Domain Adaptation In Medical
Imaging [0.709016563801433]
We propose an unsupervised method for robust domain adaptation in brain MRI segmentation by leveraging MRI-specific augmentation techniques.
The results show that our proposed approach achieves high accuracy, exhibits broad applicability, and showcases remarkable robustness against domain shift in various tasks.
arXiv Detail & Related papers (2023-08-08T17:00:11Z) - Consistency Regularization for Generalizable Source-free Domain
Adaptation [62.654883736925456]
Source-free domain adaptation (SFDA) aims to adapt a well-trained source model to an unlabelled target domain without accessing the source dataset.
Existing SFDA methods ONLY assess their adapted models on the target training set, neglecting the data from unseen but identically distributed testing sets.
We propose a consistency regularization framework to develop a more generalizable SFDA method.
arXiv Detail & Related papers (2023-08-03T07:45:53Z) - Domain Generalization for Mammographic Image Analysis with Contrastive
Learning [62.25104935889111]
The training of an efficacious deep learning model requires large data with diverse styles and qualities.
A novel contrastive learning is developed to equip the deep learning models with better style generalization capability.
The proposed method has been evaluated extensively and rigorously with mammograms from various vendor style domains and several public datasets.
arXiv Detail & Related papers (2023-04-20T11:40:21Z) - 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) - Single-domain Generalization in Medical Image Segmentation via Test-time
Adaptation from Shape Dictionary [64.5632303184502]
Domain generalization typically requires data from multiple source domains for model learning.
This paper studies the important yet challenging single domain generalization problem, in which a model is learned under the worst-case scenario with only one source domain to directly generalize to different unseen target domains.
We present a novel approach to address this problem in medical image segmentation, which extracts and integrates the semantic shape prior information of segmentation that are invariant across domains.
arXiv Detail & Related papers (2022-06-29T08:46:27Z) - Selecting the suitable resampling strategy for imbalanced data
classification regarding dataset properties [62.997667081978825]
In many application domains such as medicine, information retrieval, cybersecurity, social media, etc., datasets used for inducing classification models often have an unequal distribution of the instances of each class.
This situation, known as imbalanced data classification, causes low predictive performance for the minority class examples.
Oversampling and undersampling techniques are well-known strategies to deal with this problem by balancing the number of examples of each class.
arXiv Detail & Related papers (2021-12-15T18:56:39Z) - Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to
Unseen Domains [68.73614619875814]
We present a novel shape-aware meta-learning scheme to improve the model generalization in prostate MRI segmentation.
Experimental results show that our approach outperforms many state-of-the-art generalization methods consistently across all six settings of unseen domains.
arXiv Detail & Related papers (2020-07-04T07:56:02Z) - On the Benefits of Invariance in Neural Networks [56.362579457990094]
We show that training with data augmentation leads to better estimates of risk and thereof gradients, and we provide a PAC-Bayes generalization bound for models trained with data augmentation.
We also show that compared to data augmentation, feature averaging reduces generalization error when used with convex losses, and tightens PAC-Bayes bounds.
arXiv Detail & Related papers (2020-05-01T02:08:58Z)
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.