Source-free unsupervised domain adaptation (SF-UDA), which relies only on a pre-trained source model and unlabeled target data, has gained significant attention. Pseudo-labeling, valued for its simplicity and effectiveness, is a key approach in SF-UDA. However, existing methods neglect the consistency priors of anatomical features across samples, leading them fail to revise of high-confidence noise in structurally inconsistent regions, ultimately manifesting as significant discrepancies in pseudo-labeled samples especially in limited source data scenarios. Motivated by this insight, we propose a novel Geometric Correspondence Constrained (GCC) pseudo-labeling framework. GCC first stratifies pseudo-labeled samples into high/low-quality subsets. It then refines low-quality samples by leveraging the anatomical features inherent in high-quality samples while injecting Gaussian perturbation to perturb high-confidence noise towards the decision boundaries. This process effectively mitigates high-confidence noise disruptive effect and preserves critical prior anatomical knowledge, making it particularly powerful for scenarios with limited source data. Experiments on cross-domain fundus image datasets demonstrate that our method achieves state-of-the-art performance.

The performance of medical image segmentation is critical for subsequent analysis and decision-making in clinical practice. Although great successes have been achieved by employing deep neural networks trained with large-scale annotated datasets, the cost of collecting such datasets is usually expensive, which makes the application of these methods challenging. To address this difficulty, we propose a double-branch framework from the perspective of integrating data annotation and model construction. For the data annotation, to preserve more information as well as high efficiency, a dual polygon annotation approach is employed from the view of sparse annotating and multi-rater learning. In the generated dual polygon annotations, a larger annotation contains all the information about the lesion area, while the smaller annotation is inside the lesion area as much as possible and does not contain any background information. For the model construction, to utilize the polygon annotations efficiently, a framework included one encoder and two different decoders is used to learn agreement information between two annotations in an adversarial manner, while facilitating the learning of disagreement information. To evaluate the effectiveness of the proposed method, extensive experiments are conducted on the ISIC2017, ISIC2018, and KvasirSeg datasets. Experimental results demonstrate that the proposed method can achieve comparable or even superior performance than the fully-supervised methods.

Due to the exorbitant expense of obtaining labeled data in the field of medical image analysis, semi-supervised learning has emerged as a favorable method for the segmentation of anatomical structures. Although semi-supervised learning techniques have shown great potential in this field, existing methods only utilize image-level spatial consistency to impose unsupervised regularization on data in label space. Considering that anatomical structures often possess inherent anatomical properties that have not been focused on in previous works, this study introduces the inherent consistency into semi-supervised anatomical structure segmentation. First, the prediction and the ground-truth are projected into an embedding space to obtain latent representations that encapsulate the inherent anatomical properties of the structures. Then, two inherent consistency constraints are designed to leverage these inherent properties by aligning these latent representations. The proposed method is plug-and-play and can be seamlessly integrated with existing methods, thereby collaborating to improve segmentation performance and enhance the anatomical plausibility of the results. To evaluate the effectiveness of the proposed method, experiments are conducted on three public datasets (ACDC, LA, and Pancreas). Extensive experimental results demonstrate that the proposed method exhibits good generalizability and outperforms several state-of-the-art methods.

Automatic vessel segmentation in the fundus images plays an important role in the screening, diagnosis, treatment, and evaluation of various cardiovascular and ophthalmologic diseases. However, due to the limited well-annotated data, varying size of vessels, and intricate vessel structures, retinal vessel segmentation has become a long-standing challenge. In this paper, a novel deep learning model called AACA-MLA-D-UNet is proposed to fully utilize the low-level detailed information and the complementary information encoded in different layers to accurately distinguish the vessels from the background with low model complexity. The architecture of the proposed model is based on U-Net, and the dropout dense block is proposed to preserve maximum vessel information between convolution layers and mitigate the over-fitting problem. The adaptive atrous channel attention module is embedded in the contracting path to sort the importance of each feature channel automatically. After that, the multi-level attention module is proposed to integrate the multi-level features extracted from the expanding path, and use them to refine the features at each individual layer via attention mechanism. The proposed method has been validated on the three publicly available databases, i.e. the DRIVE, STARE, and CHASE _ DB1. The experimental results demonstrate that the proposed method can achieve better or comparable performance on retinal vessel segmentation with lower model complexity. Furthermore, the proposed method can also deal with some challenging cases and has strong generalization ability.

Class imbalance is a common problem in real-world image classification problems, some classes are with abundant data, and the other classes are not. In this case, the representations of classifiers are likely to be biased toward the majority classes and it is challenging to learn proper features, leading to unpromising performance. To eliminate this biased feature representation, many algorithm-level methods learn to pay more attention to the minority classes explicitly according to the prior knowledge of the data distribution. In this article, an attention-based approach called deep attention-based imbalanced image classification (DAIIC) is proposed to automatically pay more attention to the minority classes in a data-driven manner. In the proposed method, an attention network and a novel attention augmented logistic regression function are employed to encapsulate as many features, which belongs to the minority classes, as possible into the discriminative feature learning process by assigning the attention for different classes jointly in both the prediction and feature spaces. With the proposed object function, DAIIC can automatically learn the misclassification costs for different classes. Then, the learned misclassification costs can be used to guide the training process to learn more discriminative features using the designed attention networks. Furthermore, the proposed method is applicable to various types of networks and data sets. Experimental results on both single-label and multilabel imbalanced image classification data sets show that the proposed method has good generalizability and outperforms several state-of-the-art methods for imbalanced image classification.

Neural Architecture Search (NAS), which aims at automatically designing neural architectures, recently draw a growing research interest. Different from conventional NAS methods, in which a large number of neural architectures need to be trained for evaluation, the one-shot NAS methods only have to train one supernet which synthesizes all the possible candidate architectures. As a result, the search efficiency could be significantly improved by sharing the supernet's weights during the candidate architectures' evaluation. This strategy could greatly speed up the search process but suffer a challenge that the evaluation based on sharing weights is not predictive enough. Recently, pruning the supernet during the search has been proven to be an efficient way to alleviate this problem. However, the pruning direction in complex-structured search space remains unexplored. In this paper, we revisited the role of path dropout strategy, which drops the neural operations instead of the neurons, in supernet training, and several interesting characters of the supernet trained with dropout are found. Based on the observations, a Hierarchically-Ordered Pruning Neural Architecture Search (HOPNAS) algorithm is proposed by dynamically pruning the supernet with a proper pruning direction. Experimental results indicate that our method is competitive with state-of-the-art approaches on CIFAR10 and ImageNet.

Motion models have been proved to be a crucial part in the visual tracking process. In recent trackers, particle filter and sliding windows-based motion models have been widely used. Treating motion models as a sequence prediction problem, we can estimate the motion of objects using their trajectories. Moreover, it is possible to transfer the learned knowledge from annotated trajectories to new objects. Inspired by recent advance in deep learning for visual feature extraction and sequence prediction, we propose a trajectory predictor to learn prior knowledge from annotated trajectories and transfer it to predict the motion of target objects. In this predictor, convolutional neural networks extract the visual features of target objects. Long short-term memory model leverages the annotated trajectory priors as well as sequential visual information, which includes the tracked features and center locations of the target object, to predict the motion. Furthermore, to extend this method to videos in which it is difficult to obtain annotated trajectories, a dynamic weighted motion model that combines the proposed trajectory predictor with a random sampler is proposed. To evaluate the transfer performance of the proposed trajectory predictor, we annotated a real-world vehicle dataset. Experiment results on both this real-world vehicle dataset and an online tracker benchmark dataset indicate that the proposed method outperforms several state-of-the-art trackers.