Ning Shen

In remote sensing applications, change detection serves as a pivotal technology to monitor changes on the land surface by identifying and extracting temporal discrepancies in bitemporal images captured over the same geographical area. Although existing deep learning methods can extract temporal difference features from bitemporal images, these methods often suffer from the attenuation of feature pair distances during long-range encoding and decoding, leading to weakened deep supervision signals. To address this core challenge, this study proposes a pioneering dual-branch temporal deep supervision network, termed DTDNet. The framework employs two independent branches—a front-end semantic branch and a back-end feature mining branch—to achieve multi-dimensional coupling of temporal change features. The former focuses on precise extraction of temporal difference semantics, while the latter is dedicated to effective mining of deep features from bitemporal images. Furthermore, a feature-enhancing Attention Module (FEAM) is innovatively introduced to capture long-range dependencies among token features and learn distance mappings between bitemporal feature pairs, significantly enhancing feature discriminability. Extensive experiments on the MACD, CDD, and SYSU-CD datasets demonstrate that DTDNet achieves breakthrough performance across multiple metrics, substantially advancing the state of the art in change detection technology.

Recent advancements in semi-supervised federated learning (SSFL) have significantly enhanced public health services by enabling medical institutions to share model updates via a central server. However, most SSFL approaches are based on conservative assumptions, such as labels-at-server and labels-at-client, which fail to fully capture the complex and diverse data distributions inherent in medical institutions. To address this limitation, we introduce a novel application of SSFL tailored to a realistic client data scenario, encompassing clients with fully-labeled, partially-labeled, and fully-unlabeled data. This approach effectively navigates varying levels of data annotation by maximizing the utility of unlabeled samples within the client federation. To tackle the challenges posed by such a complex scenario, we propose a new SSFL framework, FedCD. FedCD incorporates three client-distilled models, each corresponding to a distinct client data distribution, alongside server-client federation. First, each client-distilled model condenses the diverse parameters of the client federation into robust knowledge through distillation. The contribution of each client model is then dynamically adjusted based on its proximity to the client-distilled model, ensuring that the framework adapts to the heterogeneous characteristics of individual clients. By aggregating client-distilled models, FedCD implements model drift correction, effectively mitigating parameter drift across heterogeneous models. This dynamic federated approach not only harnesses unlabeled data efficiently but also accommodates diverse annotation levels while adapting to varying data distributions. Extensive experiments on two medical image segmentation tasks and one classification task demonstrate the superiority of our method, highlighting its ability to address realistic challenges in medical data scenarios.

Fine-grained segmentation of remote sensing mineral images plays a crucial role in the investigation and monitoring of mineral resource. In deep learning methods, fine-grained segmentation and edge detection are closely related in both data construction and feature extraction. However, the open-pit mineral areas in remote sensing images are heavily affected by complex natural environmental interference, posing challenges for precise data annotation and dataset construction. In view of this, we introduce the Fine-RSMI dataset, which includes a total of 10,225 images with finely annotated edges, while also introducing challenges such as multi-scale and edge irregularities. To tackle the challenge of fine-grained segmentation in irregular edges, we propose a hierarchical fusion edge feature enhancement framework. Our framework consists of an Edge Detail Feature Enhancement Module (EDFEM) and an Edge Supervision Module (ESM). EDFEM vertically cascades multiple feature fusion units to obtain high-order complementary information for refining edge features. ESM further supervises network reinforcement learning of mineral area edges using ground truth edge maps to improve edge segmentation performance. Both modules work in a plug-and-play manner, enabling effortless integration into existing segmentation networks. Our method achieves further performance improvement in many general remote sensing segmentation frameworks, reaching the best results of 74.12% mIoU on Fine-RSMI dataset and 78.64% mAcc on WHDLD dataset. Fine-RSMI dataset and code will be available at https://github.com/chenmu1204/czx.

With the growing threat of unmanned aerial vehicle (UAV) intrusions, the topic of anti-UAV tracking has received widespread attention from the community. Traditional Siamese trackers struggle with small UAV targets and are plagued by model degradation issues. To mitigate this, we propose a novel searching region-free and template-free Siamese network (SiamSRT) to track UAV targets in thermal infrared (TIR) videos. The proposed tracker builds a two-stage Siamese architecture with the former providing detection of the first-frame ground truth by using a cross-correlated region proposal network (C-C RPN) and the latter providing detection of previous-frame predictions via a similarity-learning region convolutional neural network (S-L RCNN). In both stage, global proposals are acquired by region of interest (ROI) alignment operation to break the limitation of searching region. Then, a spatial location consistency function is introduced to suppress background thermal distractors and a temporal memory bank (TMB) is utilized to avoid template update degradation problem. Further, a single-category foreground detector (SCFD) is designed to independently predict the position of the UAV target. SCFD can re-initialize the tracker without the given target in the first frame, which can help to recover the tracking failures. Comprehensive experiments demonstrate that SiamSRT achieves the best performance compared to the most advanced algorithms in the anti-UAV tracking missions.

In medical image analysis, blood vessel segmentation is of considerable clinical value for diagnosis and surgery. The predicaments of complex vascular structures obstruct the development of the field. Despite many algorithms have emerged to get off the tight corners, they rely excessively on careful annotations for tubular vessel extraction. A practical solution is to excavate the feature information distribution from unlabeled data. This work proposes a novel semi-supervised vessel segmentation framework, named EXP-Net, to navigate through finite annotations. Based on the training mechanism of the Mean Teacher model, we innovatively engage an expert network in EXP-Net to enhance knowledge distillation. The expert network comprises knowledge and connectivity enhancement modules, which are respectively in charge of modeling feature relationships from global and detailed perspectives. In particular, the knowledge enhancement module leverages the vision transformer to highlight the long-range dependencies among multi-level token components; the connectivity enhancement module maximizes the properties of topology and geometry by skeletonizing the vessel in a non-parametric manner. The key components are dedicated to the conditions of weak vessel connectivity and poor pixel contrast. Extensive evaluations show that our EXP-Net achieves state-of-the-art performance on subcutaneous vessel, retinal vessel, and coronary artery segmentations. Code is available at https://github.com/shennbit/EXP-Net .

Automatic subcutaneous vessel imaging with near-infrared (NIR) optical apparatus can promote the accuracy of locating blood vessels, thus significantly contributing to clinical venipuncture research. Though deep learning models have achieved remarkable success in medical image segmentation, they still struggle in the subfield of subcutaneous vessel segmentation due to the scarcity and low-quality of annotated data. To relieve it, this work presents a novel semi-supervised learning framework, SCANet, that achieves accurate vessel segmentation through an alternate training strategy. The SCANet is composed of a multi-scale recurrent neural network that embeds coarse-to-fine features and two auxiliary branches, a consistency decoder and an adversarial learning branch, responsible for strengthening fine-grained details and eliminating differences between ground-truths and predictions, respectively. Equipped with a novel semi-supervised alternate training strategy, the three components work collaboratively, enabling SCANet to accurately segment vessel regions with only a handful of labeled data and abounding unlabeled data. Moreover, to mitigate the shortage of annotated data in this field, we provide a new subcutaneous vessel dataset, VESSEL-NIR. Extensive experiments on a wide variety of tasks, including the segmentation of subcutaneous vessels, retinal vessels, and skin lesions, well demonstrate the superiority and generality of our approach.

Automatic diabetic retinopathy (DR) lesions segmentation makes great sense of assisting ophthalmologists in diagnosis. Although many researches have been conducted on this task, most prior works paid too much attention to the designs of networks instead of considering the pathological association for lesions. Through investigating the pathogenic causes of DR lesions in advance, we found that certain lesions are closed to specific vessels and present relative patterns to each other. Motivated by the observation, we propose a relation transformer block (RTB) to incorporate attention mechanisms at two main levels: a self-attention transformer exploits global dependencies among lesion features, while a cross-attention transformer allows interactions between lesion and vessel features by integrating valuable vascular information to alleviate ambiguity in lesion detection caused by complex fundus structures. In addition, to capture the small lesion patterns first, we propose a global transformer block (GTB) which preserves detailed information in deep network. By integrating the above blocks of dual-branches, our network segments the four kinds of lesions simultaneously. Comprehensive experiments on IDRiD and DDR datasets well demonstrate the superiority of our approach, which achieves competitive performance compared to state-of-the-arts.