Segmentation of Mouse Brain Slices with Unsupervised Domain Adaptation Considering Cross-sectional Locations

Unsupervised domain adaptation (UDA) methods aim to reduce annotation efforts when generalizing deep learning models to new domains. UDA has been widely studied in medical image domains. However, UDA on graph domains has not been investigated yet. In this paper, we present the first attempt of unsupervised graph domain adaptation in medical imaging, with application to neurodevelopmental disorders (NDs) diagnosis, i.e. differentiating NDs patients from normal controls. It is of great importance to developing UDA methods for NDs because acquiring accurate diagnosis or labels of NDs can be difficult. In our work, we focus on Autism spectrum disorder and attention-deficit/hyperactivity disorder which are the two most common and frequently co-occurred NDs. We propose an unsupervised graph domain adaptation network (UGDAN) that consists of three main components including graph isomorphism encoders, progressive feature alignment, and un-supervised infomax regularizer. The progressive feature alignment module is designed to align graph representations of the source and target domains progressively and effectively, while the unsupervised infomax regularizer is introduced to further enhance the feature alignment by learning good unsupervised graph embeddings. We validate the proposed method with two experimental settings, cross-site adaptation and cross-disease adaptation, on two publicly available datasets. The experimental results reveal that the proposed UGDAN can achieve comparable performance compared to supervised methods trained on the target domain.

Domain adaptation (DA) has been widely investigated as a framework to alleviate the laborious task of data annotation for image segmentation. Most DA investigations operate under the unsupervised domain adaptation (UDA) setting, where the modeler has access to a large cohort of source domain labeled data and target domain data with no annotations. UDA techniques exhibit poor performance when the domain gap, i.e., the distribution overlap between the data in source and target domain is large. We hypothesize that the DA performance gap can be improved with the availability of a small subset of labeled target domain data. In this paper, we systematically investigate the impact of varying amounts of labeled target domain data on the performance gap for DA. We specifically focus on the problem of segmenting eye-regions from eye images collected using two different head mounted display systems. Source domain is comprised of 12,759 eye images with annotations and target domain is comprised of 4,629 images with varying amounts of annotations. Experiments are performed to compare the impact on DA performance gap under three schemes: unsupervised (UDA), supervised (SDA) and semi-supervised (SSDA) domain adaptation. We evaluate these schemes by measuring the mean intersection-over-union (mIoU) metric. Using only 200 samples of labeled target data under SDA and SSDA schemes, we show an improvement in mIoU of 5.4% and 6.6% respectively, over mIoU of 81.7% under UDA. By using all available labeled target data, models trained under SSDA achieve a competitive mIoU score of 89.8%. Overall, we conclude that availability of a small subset of target domain data with annotations can substantially improve DA performance.

Unsupervised domain adaptation (UDA) is an emerging learning paradigm that models on unlabeled datasets by leveraging model knowledge built on other labeled datasets, in which the statistical distributions of these datasets are usually not identical. Formally, UDA is to leverage knowledge from a labeled source domain to promote an unlabeled target domain. Although there have been a variety of methods proposed to address the UDA problem, most of them are dedicated to single-source-to-single-target domain, while the works on single-source-to-multitarget domain are relatively rare. Compared to the single-source domain with single-target domain scenario, the UDA from single-source domain to multitarget domain is more challenging since it needs to consider not only the relationships between the source and the target domains but also those among the target domains. To this end, this article proposes a kind of dictionary learning-based unsupervised multitarget domain adaptation method (DL-UMTDA). In DL-UMTDA, a common dictionary is constructed to correlate the single-source and multitarget domains, while individual dictionaries are designed to exploit the private knowledge for the target domains. Through learning the corresponding dictionary representation coefficients in the UDA process, the correlations from the source to the target domains as well as these potential relationships between the target domains can be effectively exploited. In addition, we design an alternating algorithm to solve the DL-UMTDA model with theoretical convergence guarantee. Finally, extensive experiments on benchmark (Office + Caltech) and real datasets (AgeDB, Morph, and CACD) validate the superiority of the proposed method.

Rainforest: A three-stage distribution adaptation framework for unsupervised time series domain adaptation

Unsupervised domain adaptation (UDA) for person re-identification (re-ID) is a challenging task due to large variations in human classes, illuminations, camera views, and so on. Currently, existing UDA methods focus on two-domain adaptation and are generally trained on one labeled source set and adapted on the other unlabeled target set. In this article, we put forward a new issue on person re-ID, namely, unsupervised multi-target domain adaptation (UMDA). It involves one labeled source set and multiple unlabeled target sets, which is more reasonable for practical real-world applications. Enabling UMDA has to learn the consistency for multiple domains, which is significantly different from the UDA problem. To ensure distribution consistency and learn the discriminative embedding, we further propose the Camera Identity-guided Distribution Consistency method that performs an alignment operation for multiple domains. The camera identities are encoded into the image semantic information to facilitate the adaptation of features. According to our knowledge, this is the first attempt on the unsupervised multi-target domain adaptation learning. Extensive experiments are executed on Market-1501, DukeMTMC-reID, MSMT17, PersonX, and CUHK03, and our method has achieved very competitive re-ID accuracy in multi-target domains against numerous state-of-the-art methods.

Unsupervised domain adaptation (UDA) seeks to alleviate the problem of domain shift between the distribution of unlabeled data from the target domain w.r.t. labeled data from the source domain. While the single-target UDA scenario is well studied in the literature, Multi-Target Domain Adaptation (MTDA) remains largely unexplored despite its practical importance, e.g., in multi-camera video-surveillance applications. The MTDA problem can be addressed by adapting one specialized model per target domain, although this solution is too costly in many real-world applications. Blending multiple targets for MTDA has been proposed, yet this solution may lead to a reduction in model specificity and accuracy. In this paper, we propose a novel unsupervised MTDA approach to train a CNN that can generalize well across multiple target domains. Our Multi-Teacher MTDA (MT-MTDA) method relies on multi-teacher knowledge distillation (KD) to iteratively distill target domain knowledge from multiple teachers to a common student. The KD process is performed in a progressive manner, where the student is trained by each teacher on how to perform UDA for a specific target, instead of directly learning domain adapted features. Finally, instead of combining the knowledge from each teacher, MT-MTDA alternates between teachers that distill knowledge, thereby preserving the specificity of each target (teacher) when learning to adapt to the student. MT-MTDA is compared against state- of-the-art methods on several challenging UDA benchmarks, and empirical results show that our proposed model can provide a considerably higher level of accuracy across multiple target domains. Our code is available at: https://gi.thub.com/LIVIAETS/MT-MTDA.

We address the task of unsupervised domain adaptation (UDA) for videos with self-supervised learning. While UDA for images is a widely studied problem, UDA for videos is relatively unexplored. In this paper, we propose a novel self-supervised loss for the task of video UDA. The method is motivated by inverted reasoning. Many works on video classification have shown success with representations based on events in videos, e.g., ‘reaching’, ‘picking’, and ‘drinking’ events for ‘drinking coffee’. We argue that if we have event-based representations, we should be able to predict the relative distances between clips in videos. Inverting that, we propose a self-supervised task to predict the difference of the distance between two clips from the source video and the distance between two clips from the target video. We hope that such a task would encourage learning event-based representations of the videos, which is known to be beneficial for classification. Since we predict the difference of clip distances between clips from source videos and target videos, we ‘tie’ the two domains and expect to achieve well-adapted representations. We combine this purely self-supervised loss and the source classification loss to learn the model parameters. We give extensive empirical results on challenging video UDA benchmarks, i.e., UCF-HMDB and EPIC-Kitchens. The presented qualitative and quantitative results support our motivations and method.

Point cloud classification is a popular task in 3D vision. However, previous works, usually assume that point clouds at test time are obtained with the same procedure or sensor as those at training time. Unsupervised Domain Adaptation (UDA) instead, breaks this assumption and tries to solve the task on an unlabeled target domain, leveraging only on a supervised source domain. For point cloud classification, recent UDA methods try to align features across domains via auxiliary tasks such as point cloud reconstruction, which however do not optimize the discriminative power in the target domain in feature space. In contrast, in this work, we focus on obtaining a discriminative feature space for the target domain enforcing consistency between a point cloud and its augmented version. We then propose a novel iterative self-training methodology that exploits Graph Neural Networks in the UDA context to refine pseudo-labels. We perform extensive experiments and set the new state-of-the art in standard UDA benchmarks for point cloud classification. Finally, we show how our approach can be extended to more complex tasks such as part segmentation.

Recently unsupervised domain adaptation for the semantic segmentation task has become more and more popular due to high-cost of pixel-level annotation on real-world images. However, most domain adaptation methods are only restricted to single-source-single-target pair, and can not be directly extended to multiple target domains. In this work, we propose a collaborative learning framework to achieve unsupervised multi-target domain adaptation. An unsupervised domain adaptation expert model is first trained for each source-target pair and is further encouraged to collaborate with each other through a bridge built between different target domains. These expert models are further improved by adding the regularization of making the consistent pixel-wise prediction for each sample with the same structured context. To obtain a single model that works across multiple target domains, we propose to simultaneously learn a student model which is trained to not only imitate the output of each expert on the corresponding target domain, but also to pull different expert close to each other with regularization on their weights. Extensive experiments demonstrate that the proposed method can effectively exploit rich structured information contained in both labeled source domain and multiple unlabeled target domains. Not only does it perform well across multiple target domains but also performs favorably against state-of-the-art unsupervised domain adaptation methods specially trained on a single source-target pair. Code is available at https://github.com/junpan19/MTDA.

<p>Unsupervised medical domain adaptation (DA) is an important field of study in medical image analysis, as domain shift is a very common issue for medical imaging. Unsupervised domain adaptation for the purpose of image segmentation on an unseen target domain has shown to be effective for brain MR scan problems. To improve the performance of unsupervised medical DA for segmentation, a Structure Preserving Cycle-GAN (SP Cycle-GAN) implementation was introduced. The SP Cycle-GAN was used to adapt STARE-domain retinal scans to the target DRIVE dataset domain, for the purpose of blood vessel segmentation via an Attention U-Net model. Pseudo-label generation on unlabelled source domain images was investigated to see if additional generated data could improve segmentation performance on the target domain. The implemented SP Cycle-GAN was shown to be effective for preserving structures in the source domain in translated target domain images. The SP Cycle-GAN however was not suited to the STARE and DRIVE datasets and did not outperform a simple baseline method of a trained segmentation model on the original source domain STARE data, which achieved a mean Dice Score (DSC) of 0.786 ± 0.015 on the DRIVE test dataset. The use of pseudo-labelled data to incorporate unlabelled source domain data for unsupervised DA was shown to have potential for improving performance, with a DSC of 0.718 ± 0.060 using pseudo-labelled data vs. 0.709 ± 0.063 when not using pseudo-labelled data. While the SP Cycle-GAN preserved blood vessel structure effectively, this resulted in another domain shift caused by the difference in overall shape of the scans. The introduced SP Cycle-GAN method shows promise for other DA datasets for medical image segmentation, in which small structures within an overall larger scan structure must be preserved.</p>

<p>Unsupervised medical domain adaptation (DA) is an important field of study in medical image analysis, as domain shift is a very common issue for medical imaging. Unsupervised domain adaptation for the purpose of image segmentation on an unseen target domain has shown to be effective for brain MR scan problems. To improve the performance of unsupervised medical DA for segmentation, a Structure Preserving Cycle-GAN (SP Cycle-GAN) implementation was introduced. The SP Cycle-GAN was used to adapt STARE-domain retinal scans to the target DRIVE dataset domain, for the purpose of blood vessel segmentation via an Attention U-Net model. Pseudo-label generation on unlabelled source domain images was investigated to see if additional generated data could improve segmentation performance on the target domain. The implemented SP Cycle-GAN was shown to be effective for preserving structures in the source domain in translated target domain images. The SP Cycle-GAN however was not suited to the STARE and DRIVE datasets and did not outperform a simple baseline method of a trained segmentation model on the original source domain STARE data, which achieved a mean Dice Score (DSC) of 0.786 ± 0.015 on the DRIVE test dataset. The use of pseudo-labelled data to incorporate unlabelled source domain data for unsupervised DA was shown to have potential for improving performance, with a DSC of 0.718 ± 0.060 using pseudo-labelled data vs. 0.709 ± 0.063 when not using pseudo-labelled data. While the SP Cycle-GAN preserved blood vessel structure effectively, this resulted in another domain shift caused by the difference in overall shape of the scans. The introduced SP Cycle-GAN method shows promise for other DA datasets for medical image segmentation, in which small structures within an overall larger scan structure must be preserved.</p>

It is well known that the mismatch between training (source) and test (target) data distribution will significantly decrease the performance of acoustic scene classification (ASC) systems. To address this issue, domain adaptation (DA) is one solution and many unsupervised DA methods have been proposed. These methods focus on a scenario of single source domain to single target domain. However, we will face such problem that test data comes from multiple target domains. This problem can be addressed by producing one model per target domain, but this solution is too costly. In this paper, we propose a novel unsupervised multi-target domain adaption (MTDA) method for ASC, which can adapt to multiple target domains simultaneously and make use of the underlying relation among multiple domains. Specifically, our approach combines traditional adversarial adaptation with two novel discriminator tasks that learns a common subspace shared by all domains. Furthermore, we propose to divide the target domain into the easy-to-adapt and hard-to-adapt domain, which enables the system to pay more attention to hard-to-adapt domain in training. The experimental results on the DCASE 2020 Task 1-A dataset and the DCASE 2019 Task 1-B dataset show that our proposed method significantly outperforms the previous unsupervised DA methods.

Unsupervised domain adaptive object detection aims to adapt a well-trained detector from its original source domain with rich labeled data to a new target domain with unlabeled data. Recently, mainstream approaches perform this task through adversarial learning, yet still suffer from two limitations. First, they mainly align marginal distribution by unsupervised cross-domain feature matching, and ignore each feature's categorical and positional information that can be exploited for conditional alignment; Second, they treat all classes as equally important for transferring cross-domain knowledge and ignore that different classes usually have different transferability. In this article, we propose a joint adaptive detection framework (JADF) to address the above challenges. First, an end-to-end joint adversarial adaptation framework for object detection is proposed, which aligns both marginal and conditional distributions between domains without introducing any extra hyper-parameter. Next, to consider the transferability of each object class, a metric for class-wise transferability assessment is proposed, which is incorporated into the JADF objective for domain adaptation. Further, an extended study from unsupervised domain adaptation (UDA) to unsupervised few-shot domain adaptation (UFDA) is conducted, where only a few unlabeled training images are available in unlabeled target domain. Extensive experiments validate that JADF is effective in both the UDA and UFDA settings, achieving significant performance gains over existing state-of-the-art cross-domain detection methods.

Unsupervised domain adaptation (UDA) techniques have the potential to enhance the transferability of neural network models in unknown scenarios and reduce the labelling costs associated with unlabelled datasets. Popular solutions to this challenging UDA task are adversarial training and self-training. However, current adversarial-based UDA methods emphasize only global or local feature alignment, which is insufficient for tackling the domain shift. In addition, self-training-based methods inevitably produce many wrong pseudo labels on the target domain due to bias towards the source domain. To tackle the above problems, this paper proposes a hybrid training framework that integrates global-local adversarial training and self-training strategies to effectively tackle global-local domain shift. First, the adversarial approach measures the discrepancies between domains from domain and category-level perspectives. The adversarial network incorporates discriminators at the local-category and global-domain levels, thereby facilitating global-local feature alignment through multi-level adversarial training. Second, the self-training strategy is integrated to acquire domain-specific knowledge, effectively mitigating negative migration. By combining these two domain adaptation strategies, we present a more efficient approach for mitigating the domain gap. Finally, a self-labelling mechanism is introduced to directly explore the inherent distribution of pixels, allowing for the rectification of pseudo labels generated during the self-training stage. Compared to state-of-the-art UDA methods, the proposed method gains 3.2 % , 1.21 % , 5.86 % , 6.16 % mIoU improvements on Rural → Urban, Urban → Rural, Potsdam → Vaihingen, Vaihingen → Potsdam, respectively.

Unsupervised deep domain adaptation algorithm for video based human activity recognition via recurrent neural networks