IDPL: Intra-subdomain Adaptation Adversarial Learning Segmentation Method Based on Dynamic Pseudo Labels

Conventional machine learning relies on two presumptions: (1) the training and testing datasets follow the same independent distribution, and (2) an adequate quantity of samples is essential for achieving optimal model performance during training. Nevertheless, meeting these two assumptions can be challenging in real-world scenarios. Domain adaptation (DA) is a subfield of transfer learning that focuses on reducing the distribution difference between the source domain (Ds) and target domain (Dt) and subsequently applying the knowledge gained from the Ds task to the Dt task. The majority of current DA methods aim to achieve domain invariance by aligning the marginal probability distributions of the Ds. and Dt. Recent studies have pointed out that aligning marginal probability distributions alone is not sufficient and that alignment of conditional probability distributions is equally important for knowledge migration. Nonetheless, unsupervised DA presents a more significant difficulty in aligning the conditional probability distributions because of the unavailability of labels for the Dt. In response to this issue, there have been several proposed methods by researchers, including pseudo-labeling, which offer novel solutions to tackle the problem. In this paper, we systematically analyze various pseudo-labeling algorithms and their applications in unsupervised DA. First , we summarize the pseudo-label generation methods based on the single and multiple classifiers and actions taken to deal with the problem of imbalanced samples. Second, we investigate the application of pseudo-labeling in category feature alignment and improving feature discrimination. Finally, we point out the challenges and trends of pseudo-labeling algorithms. As far as we know, this article is the initial review of pseudo-labeling techniques for unsupervised DA.

Pseudo-labelling is a popular technique in unsupervised domain adaptation for semantic segmentation. However, pseudo labels are noisy and inevitably have confirmation bias due to the discrepancy between source and target domains and training process. In this paper, we train the model by the pseudo labels which are implicitly produced by itself to learn new complementary knowledge about target domain. Specifically, we propose a tri-learning architecture, where every two branches produce the pseudo labels to train the third one. And we align the pseudo labels based on the similarity of the probability distributions for each two branches. To further implicitly utilize the pseudo labels, we maximize the distances of features for different classes and minimize the distances for the same classes by triplet loss. Extensive experiments on GTA5 to Cityscapes and SYNTHIA to Cityscapes tasks show that the proposed method has considerable improvements.

Unsupervised Domain Adaptation (UDA) has attracted a surge of interest over the past decade but is difficult to be used in real-world applications. Considering the privacy-preservation issues and security concerns, in this work, we study a practical problem of Source-Free Domain Adaptation (SFDA), which eliminates the reliance on annotated source data. Current SFDA methods focus on extracting domain knowledge from the source-trained model but neglects the intrinsic structure of the target domain. Moreover, they typically utilize pseudo labels for self-training in the target domain, but suffer from the notorious error accumulation problem. To address these issues, we propose a new SFDA framework, called Region-to-Pixel Adaptation Network (RPANet), which learns the region-level and pixel-level discriminative representations through coarse-to-fine self-supervision. The proposed RPANet consists of two modules, Foreground-aware Contrastive Learning (FCL) and Confidence-Calibrated Pseudo-Labeling (CCPL), which explicitly address the key challenges of “how to distinguish” and “how to refine”. To be specific, FCL introduces a supervised contrastive learning paradigm in the region level to contrast different region centroids across different target images, which efficiently involves all pseudo labels while robust to noisy samples. CCPL designs a novel fusion strategy to reduce the overconfidence problem of pseudo labels by fusing two different target predictions without introducing any additional network modules. Extensive experiments on three cross-domain polyp segmentation tasks reveal that RPANet significantly outperforms state-of-the-art SFDA and UDA methods without access to source data, revealing the potential of SFDA in medical applications.

Superpixel-guided class-level denoising for unsupervised domain adaptive fundus image segmentation without source data

Domain Adaptation (DA) is important for a segmentation model to deal with domain shift in a new target domain. Due to the privacy concern of medical data and the expensive annotation process, Source-Free Domain Adaptation (SFDA) is appealing without access to source data and labels of target domain images for the adaptation. However, existing SFDA methods have limited performance due to insufficient supervision and unreliable pseudo labels. In this paper, we propose an enhanced Iterative Pseudo Label Correction (IPLC+) SFDA framework guided by Segment Anything Model (SAM) for medical image segmentation. Specifically, with a pre-trained source model and SAM, we propose a Reliable SAM Pseudo-label Generator (RSPG) to obtain high-quality and reliable pseudo labels in the target domain based on multiple prompts randomly sampled from the model's prediction. To provide more efficient constraints during adaptation, we introduce self-training pseudo labels weighted by the uncertainty, and propose regularization using mean curvature minimization based on shape-prior knowledge for smoother segmentation. We also propose an Iterative Correction Learning (ICL) strategy to iteratively refine pseudo labels using SAM with updated prompts and combine supervisions to optimize the model sufficiently. Experiments on two public multi-site datasets for prostate and heart segmentation show that our method effectively outperformed ten state-of-the-art SFDA methods, improved the quality of pseudo labels, and even achieved better results than fully supervised learning in the target domain in some cases.

Multi-source domain adaptation (MSDA) plays an important role in industrial model generalization. Recent efforts regarding MSDA focus on enhancing multi-domain distributional alignment while omitting three issues, e.g., the class-level discrepancy quantification, the unavailability of noisy pseudo labels, and source transferability discrimination, potentially resulting in suboptimal adaption performance. Therefore, we address these issues by proposing a prototype aggregation method that models the discrepancy between source and target domains at the class and domain levels. Our method achieves domain adaptation based on a group of prototypes (i.e., representative feature embeddings). A similarity score-based strategy is designed to quantify the transferability of each domain. At the class level, our method quantifies class-specific cross-domain discrepancy according to reliable target pseudo labels. At the domain level, our method establishes distributional alignment between noisy pseudo-labeled target samples and the source domain prototypes. Therefore, adaptation at the class and domain levels establishes a complementary mechanism to obtain accurate predictions. The results on three standard benchmarks demonstrate that our method outperforms most state-of-the-art methods. In addition, we provide further elaboration of the proposed method in light of the interpretable results obtained from the analysis experiments.

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.

Electroencephalogram (EEG) signals convey abundant physiological and psychological information. Decoding EEG is fundamental for brain-computer interaction and medical rehabilitation. Nevertheless, the nonstationarity and inter-individual variability of EEG signals impedes current models from achieving robust crosssubject generalization without expensive subject-specific recalibration, thereby restricting practical deployment. Unsupervised domain adaptation (UDA) aims to improve generalization by minimizing distribution discrepancies between source and target domains. Recent studies regard different subjects as distinct domains and leverage UDA to facilitate cross-subject EEG classification by learning domain-invariant features through discrepancy minimization or adversarial training. However, these conventional methods are all developed in Euclidean space, which are insufficient to capture the non-linear structures inherent in EEG data. To address this limitation, we propose a Deep Adversarial Neural Network on the Symmetric Positive Definite (SPD) matrix manifold, referred to as SPD-DANN, which facilitates the extraction of subject-invariant features through adversarial learning. Additionally, we design an SPD domain feature alignment loss and an SPD class prototype pair loss to simultaneously promote feature alignment across subjects and enhance feature space discriminability. Extensive experiments on four brain-computer interface (BCI) datasets demonstrate that our method surpasses several state-of-the-art UDA techniques. Furthermore, the proposed loss functions are readily adaptable to broader unsupervised or semi-supervised domain adaptation frameworks.

Due to the unavailability of source domain data encountered in unsupervised domain adaptation, there has been an increasing number of studies on source-free domain adaptation (SFDA) in recent years. To better solve the SFDA problem and effectively leverage the multi-modal information in medical images, this paper presents a novel SFDA method for multi-modal stroke lesion segmentation in which evidential deep learning instead of convolutional neural network. Specifically, for multi-modal stroke images, we design a multi-modal opinion fusion module which uses Dempster-Shafer evidence theory for decision fusion of different modalities. Besides, for the SFDA problem, we use the pseudo label learning method, which obtains pseudo labels from the pre-trained source model to perform the adaptation process. To solve the unreliability of pseudo label caused by domain shift, we propose a pseudo label filtering scheme using shadowed sets theory and a pseudo label refining scheme using evidential uncertainty. These two schemes can automatically extract unreliable parts in pseudo labels and jointly improve the quality of pseudo labels with low computational costs. Experiments on two multi-modal stroke lesion datasets demonstrate the superiority of our method over other state-of-the-art SFDA methods.

Unsupervised domain adaption (UDA), which aims to enhance the segmentation performance of deep models on unlabeled data, has recently drawn much attention. In this paper, we propose a novel UDA method (namely DLaST) for medical image segmentation via disentanglement learning and self-training. Disentanglement learning factorizes an image into domain-invariant anatomy and domain-specific modality components. To make the best of disentanglement learning, we propose a novel shape constraint to boost the adaptation performance. The self-training strategy further adaptively improves the segmentation performance of the model for the target domain through adversarial learning and pseudo label, which implicitly facilitates feature alignment in the anatomy space. Experimental results demonstrate that the proposed method outperforms the state-of-the-art UDA methods for medical image segmentation on three public datasets, i.e., a cardiac dataset, an abdominal dataset and a brain dataset. The code will be released soon.

Pseudo-label-based unsupervised domain adaptation (UDA) has increasingly gained interest in medical image analysis, aiming to solve the problem of performance degradation of deep neural networks when dealing with unseen data. Although it has achieved great success, it still faced two significant challenges: improving pseudo labels' precision and mitigating the effects caused by noisy pseudo labels. To solve these problems, we propose a novel UDA framework based on label distribution learning, where the problem is formulated as noise label correcting and can be solved by converting a fixed categorical value (pseudo labels on target data) to a distribution and iteratively update both network parameters and label distribution to correct noisy pseudo labels, and then these labels are used to re-train the model. We have extensively evaluated our framework with vulnerable plaques detection between two IVOCT datasets. Experimental results show that our UDA framework is effective in improving the detection performance of unlabeled target images.

Domain adaptation (DA) is a representation learning methodology that transfers knowledge from a label-sufficient source domain to a label-scarce target domain. While most of early methods are focused on unsupervised DA (UDA), several studies on semi-supervised DA (SSDA) are recently suggested. In SSDA, a small number of labeled target images are given for training, and the effectiveness of those data is demonstrated by the previous studies. However, the previous SSDA approaches solely adopt those data for embedding ordinary supervised losses, overlooking the potential usefulness of the few yet informative clues. Based on this observation, in this paper, we propose a novel method that further exploits the labeled target images for SSDA. Specifically, we utilize labeled target images to selectively generate pseudo labels for unlabeled target images. In addition, based on the observation that pseudo labels are inevitably noisy, we apply a label noise-robust learning scheme, which progressively updates the network and the set of pseudo labels by turns. Extensive experimental results show that our proposed method outperforms other previous state-of-the-art SSDA methods.

Unsupervised domain adaptation adapts classifiers to an un-labeled target domain by exploiting a labeled source domain. To reduce discrepancy between source and target domains, adversarial learning methods are typically selected to seek domain-invariant representations by confusing the domain discriminator. However, classifiers may not be well adapted to such a domain-invariant representation space, as the sample-level and class-level data structures could be distorted during adversarial learning. In this paper, we propose a novel Transferable Feature Learning approach on Graphs (TFLG) for unsupervised adversarial domain adaptation, which jointly incorporates sample-level and class-level structure information across two domains. TFLG first constructs graphs and identifies the class-wise correspondence across domains. A novel cross-domain graph convolutional operation is designed to jointly align the sample-level and class-level structures in two domains. Extensive experiments on benchmark datasets demonstrate the effectiveness of our approach, compared to the representative unsupervised domain adaptation methods.

The performance of existing deep learning-based object detection models significantly degrades due to the influence of complex weather. To effectively eliminate the problem of domain differences caused by different weather scenes, propose a multi-domain dynamic mean teacher model. First, introduce a multi-domain mean teacher module to generate pseudo-labels for target domain data in multiple different weather scenes.Then, introduce a student network-based style transfer module to solve the problem of weak generalization ability of student network to different target domains in multi-domain tasks. It can effectively reduce the difference between source domain and different target domains and improve the generalization ability of the student network to different target domains. Finally, a teacher network-based dynamic filtering pseudo-label module is used to dynamically adjust the threshold values of filtering pseudo labels according to the learning effect of the teacher network on different target domains and improve the quality of pseudo labels for each target domain. Experiments on FoggyCityscapes & RainCityscapes and Dusk-rain & Night-rain datasets show that the proposed model achieves 40.3% and 31.4% accuracy respectively, and outperforms other comparison methods in complex weather scenes such as rain, fog and night.

Domain adaptation (DA) mitigates the domain shift problem when transferring knowledge from one annotated domain to another similar but different unlabeled domain. However, existing models often utilize one of the ImageNet models as the backbone without exploring others, and fine-tuning or retraining the backbone ImageNet model is also time-consuming. Moreover, pseudo-labeling has been used to improve the performance in the target domain, while how to generate confident pseudo labels and explicitly align domain distributions has not been well addressed. In this paper, we show how to efficiently opt for the best pre-trained features from seventeen well-known ImageNet models in unsupervised DA problems. In addition, we propose a recurrent pseudo-labeling model using the best pre-trained features (termed PRPL) to improve classification performance. To show the effectiveness of PRPL, we evaluate it on three benchmark datasets, Office+Caltech-10, Office31, and Office-Home. Extensive experiments show that our model reduces computation time and boosts the mean accuracy to 98.1%, 92.4%, and 81.2%, respectively, substantially outperforming the state of the art.