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Shuai Jiang

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4 papers

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YNICL Journal 2026 Journal Article

Remote cortical degeneration related to structural connectivity following recent small subcortical infarcts

Youjie Wang
Jingyu Cui
Yuying Yan
Tang Yang
Yue Yuan
Rumei Lei
Rongfeng Luo
Bo Wu

BACKGROUND: Secondary cortical degeneration caused by the remote effects of subcortical infarcts has been implicated in long-term outcomes after acute ischemic stroke. However, this process remains insufficiently studied in recent small subcortical infarcts (RSSI). We aimed to verify RSSI-induced cortical damage, determine whether it can be captured by neuroimaging markers, and explore its association with clinical outcomes. METHODS: RSSI patients with longitudinal Magnetic Resonance Imaging (MRI) were included. Cortical degeneration was assessed using linear mixed-effects models, incorporating a direct approach based on individual diffusion weighted imaging and an indirect approach using the normative connectome from the Human Connectome Project (HCP). Principal component analysis (PCA) was employed to extract features of cortical alterations. The resulting component scores were used in general linear models to assess associations with neuroimaging markers and clinical outcomes. RESULTS: A total of 76 RSSI patients were analyzed. RSSI was found to induce progressive cortical thinning and volume loss in structurally connected regions. PCA identified a component reflecting parenchymal atrophy associated with diffusion-based markers of white matter integrity, as well as the presence of track/cap signs. Moreover, faster cortical degeneration in lesion-connected regions was significantly associated with a greater increase in Hamilton Anxiety Rating Scale (HAMA) scores (β = -2.38, 95% CI = -4.30 - -0.47, p = 0.017). CONCLUSIONS: RSSI induces secondary cortical damage through structurally connected fiber tracts, which is detectable by neuroimaging markers of white matter integrity. These regional cortical alterations may be relevant to post-stroke outcomes and require validation in larger longitudinal studies.

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JBHI Journal 2025 Journal Article

GlanceSeg: Real-Time Microaneurysm Lesion Segmentation With Gaze-Map-Guided Foundation Model for Early Detection of Diabetic Retinopathy

Hongyang Jiang
Mengdi Gao
Zirong Liu
Chen Tang
Xiaoqing Zhang
Shuai Jiang
Wu Yuan
Jiang Liu

Early-stage diabetic retinopathy (DR) presents challenges in clinical diagnosis due to inconspicuous and minute microaneurysms (MAs), resulting in limited research in this area. Additionally, the potential of emerging foundation models, such as the segment anything model (SAM), in medical scenarios remains rarely explored. In this work, we propose a human-in-the-loop, label-free early DR diagnosis framework called GlanceSeg, based on SAM. GlanceSeg enables real-time segmentation of MA lesions as ophthalmologists review fundus images. Our human-in-the-loop framework integrates the ophthalmologist's gaze maps, allowing for rough localization of minute lesions in fundus images. Subsequently, a saliency map is generated based on the located region of interest, which provides prompt points to assist the foundation model in efficiently segmenting MAs. Finally, a domain knowledge filtering (DKF) module refines the segmentation of minute lesions. We conducted experiments on two newly-built public datasets, i. e. , IDRiD and Retinal-Lesions, and validated the feasibility and superiority of GlanceSeg through visualized illustrations and quantitative measures. Additionally, we demonstrated that GlanceSeg improves annotation efficiency for clinicians and further enhances segmentation performance through fine-tuning using annotations. The clinician-friendly GlanceSeg is able to segment small lesions in real-time, showing potential for clinical applications.

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NeurIPS Conference 2025 Conference Paper

Searching Efficient Semantic Segmentation Architectures via Dynamic Path Selection

Yuxi Liu
Min Liu
Shuai Jiang
Yi Tang
Yaonan Wang

Existing NAS methods for semantic segmentation typically apply uniform optimization to all candidate networks (paths) within a one-shot supernet. However, the concurrent existence of both promising and suboptimal paths often results in inefficient weight updates and gradient conflicts. This issue is particularly severe in semantic segmentation due to its complex multi-branch architectures and large search space, which further degrade the supernet's ability to accurately evaluate individual paths and identify high-quality candidates. To address this issue, we propose Dynamic Path Selection (DPS), a selective training strategy that leverages multiple performance proxies to guide path optimization. DPS follows a stage-wise paradigm, where each phase emphasizes a different objective: early stages prioritize convergence, the middle stage focuses on expressiveness, and the final stage emphasizes a balanced combination of expressiveness and generalization. At each stage, paths are selected based on these criteria, concentrating optimization efforts on promising paths, thus facilitating targeted and efficient model updates. Additionally, DPS integrates a dynamic stage scheduler and a diversity-driven exploration strategy, which jointly enable adaptive stage transitions and maintain structural diversity among selected paths. Extensive experiments demonstrate that, under the same search space, DPS can discover efficient models with strong generalization and superior performance.

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AAAI Conference 2020 Conference Paper

Geometry-Driven Self-Supervised Method for 3D Human Pose Estimation

Yang Li
Kan Li
Shuai Jiang
Ziyue Zhang
Congzhentao Huang
Richard Yi Da Xu

The neural network based approach for 3D human pose estimation from monocular images has attracted growing interest. However, annotating 3D poses is a labor-intensive and expensive process. In this paper, we propose a novel selfsupervised approach to avoid the need of manual annotations. Different from existing weakly/self-supervised methods that require extra unpaired 3D ground-truth data to alleviate the depth ambiguity problem, our method trains the network only relying on geometric knowledge without any additional 3D pose annotations. The proposed method follows the two-stage pipeline: 2D pose estimation and 2D-to-3D pose lifting. We design the transform re-projection loss that is an effective way to explore multi-view consistency for training the 2Dto-3D lifting network. Besides, we adopt the conﬁdences of 2D joints to integrate losses from different views to alleviate the inﬂuence of noises caused by the self-occlusion problem. Finally, we design a two-branch training architecture, which helps to preserve the scale information of re-projected 2D poses during training, resulting in accurate 3D pose predictions. We demonstrate the effectiveness of our method on two popular 3D human pose datasets, Human3. 6M and MPI- INF-3DHP. The results show that our method signiﬁcantly outperforms recent weakly/self-supervised approaches.

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