Open-set gait recognition presents a critical challenge for real-world identity authentication systems, requiring simultaneous identification of known users and detection of unknown users under practical deployment conditions. However, in practical Wi-Fi sensing environments, signal noise, clothing variation, and multipath interference often blur the boundary between known and unknown classes, making traditional closed-set methods inadequate. To address this challenge, GUARD is proposed as a unified open-set and closed-set gait recognition framework based on feature reconstruction. The core idea is that known-class samples can be accurately reconstructed under matched label conditions, while unknown samples yield significantly higher reconstruction errors due to label mismatch, thereby providing a discriminative signal for open-set recognition. To enhance the stability and discriminability of features, GUARD integrates a Global Temporal Attention (GTA) mechanism to capture long-range temporal dependencies, and introduces a Pseudo-Gaussian Enhanced Self-Attention (PGESA) module that models dynamic attention distributions via Gaussian approximation, enabling selective emphasis on salient temporal features while effectively suppressing background noise. Additionally, a feature extractor locking strategy is employed to freeze identity-relevant representations once closed-set performance is optimized, preventing degradation during open-set training. Experimental results show that GUARD achieves over 20% improvement in open-set recognition rate, while maintaining approximately 95% closed-set accuracy, demonstrating superior robustness and generalization in complex sensing environments.

Accurate target segmentation from computed tomography (CT) scans is crucial for surgical robots to perform clinical surgeries successfully. However, the lack of medical image data and annotations has been the biggest obstacle to learning robust medical image segmentation models. Self-supervised learning can effectively address this problem by providing a strategy to pre-train a model with unlabeled data, and then fine-tune downstream tasks with limited labeled data. Existing self-supervised methods fail to simultaneously utilize the abundant global anatomical structure information and local feature differences in medical imaging. In this work, we propose a new strategy for the pre-training framework, which uses the three-dimensional anatomical structure of medical images and specific task and background cues to segment volumetric medical images with limited annotations. Specifically, we propose (1) learning intrinsic patterns of volumetric medical image structures through multiple sub-tasks, and (2) designing a multi-level background cube contrastive learning strategy to enhance the target feature representation by exploiting the differences between the specific target and background. We conduct extensive evaluations on two publicly available datasets. Under limited annotation settings, the proposed method yields significant improvements compared to other self-supervised learning techniques. The proposed method achieves within 6% of the baseline performance using only five labeled CT volumes for training.

Accurately segmenting various clinically significant lesion areas from whole-body computed tomography (CT) scans is crucial for automated diagnosis and treatment planning. Training an automatic segmentation model effectively is desirable, but it heavily relies on a large scale of pixel-wise labeled data, which is laborious, time-consuming, and expensive to obtain. Existing weakly-supervised segmentation approaches often struggle with regions nearby the lesion boundaries. This paper proposes a target-level incomplete annotation (TIA) for medical image annotation and a multi-lesion segmentation framework. TIA annotates only one complete target region per slice to accurately capture boundaries with minimal annotated effort. Multi-lesion segmentation framework is a weakly supervised learning method, which first implements a medical cut-paste segmentation branch to provide images with pure target pixels and boundaries for training the lesion segmentation model, second utilizes prior anatomical information in the prior-assisted target localization branch to locate and identify target regions, third generates high-confidence pseudo-labels by combining the outputs of cut-paste segmentation branch and prior-assisted target localization branch. A graph neural network (GNN) is adopted to correct noisy labels and propagate reliably labeled pixels to unlabeled pixels. By utilizing TIA, our framework can achieve state-of-the-art results for medical image segmentation, which is validated on Crohn's dataset.

Medical images acquired under suboptimal conditions often suffer from quality degradation, such as low-light, blurring, and artifacts. Such degradations obscure the lesions and anatomical structures in medical images, making it difficult to distinguish key pathological regions. This significantly increases the risk of misdiagnosis by automated medical diagnostic systems or clinicians. To address this challenge, we propose a multi-Color space-based quality enhancement network (MSQNet) that effectively eliminates global low-quality factors while preserving pathology-related characteristics for improved clinical observation and analysis. We first revisit the properties of image quality enhancement in different color spaces, where the V-channel in the HSV space can better represent the contrast and brightness enhancement process, whereas the A/B-channel in the LAB space is more focused on the color change of low-quality images. The proposed framework harnesses the unique properties of different color spaces to optimize the image enhancement process. Specifically, we propose a pathology-preserving transformer, designed to selectively aggregate features across different color spaces and enable comprehensive multiscale feature fusion. Leveraging these capabilities, MSQNet effectively enhances low-quality RGB medical images while preserving key pathological features, thereby establishing a new paradigm in medical image enhancement. Extensive experiments on three public medical image datasets demonstrate that MSQNet outperforms traditional enhancement techniques and state-of-the-art methods, in terms of both quantitative metrics and qualitative visual assessment. MSQNet successfully improves image quality while preserving pathological features and anatomical structures, facilitating accurate diagnosis and analysis by medical professionals and automated systems.

To accurately segment various clinical lesions from computed tomography(CT) images is a critical task for the diagnosis and treatment of many diseases. However, current segmentation frameworks are tailored to specific diseases, and limited frameworks can detect and segment different types of lesions. Besides, it is another challenging problem for current segmentation frameworks to segment visually inconspicuous and small-scale tumors (such as small intestinal stromal tumors and pancreatic tumors). Our proposed framework, CDI-NSTSEG, efficiently segments small non-salient tumors using multi-scale visual information and non-local target mining. CDI-NSTSEG follows the diagnostic process of clinicians, including preliminary screening, localization, refinement, and segmentation. Specifically, we first explore to extract the unique features at three different scales (1×, 0.5×, and 1.5×) based on the scale space theory. Our proposed scale fusion module (SFM) hierarchically fuses features to obtain a comprehensive representation, similar to preliminary screening in clinical diagnosis. The global localization module (GLM) is designed with a non-local attention mechanism. It captures the long-range semantic dependencies of channels and spatial locations from the fused features. GLM enables us to locate the tumor from a global perspective and output the initial prediction results. Finally, we design the layer focusing module (LFM) to gradually refine the initial results. LFM mainly conducts context exploration based on foreground and background features, focuses on suspicious areas layer-by-layer, and performs element-by-element addition and subtraction to eliminate errors. Our framework achieves state-of-the-art segmentation performance on small intestinal stromal tumor and pancreatic tumor datasets.