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Ying Hu

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15 papers

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TIST Journal 2026 Journal Article

Personalized Forgetting Mechanism with Concept-Driven Knowledge Tracing

Shanshan Wang
Ying Hu
Qianru Li
Xun Yang
Zhongzhou Zhang
Keyang Wang
Xingyi Zhang

Knowledge Tracing (KT) aims to trace changes in students’ knowledge states throughout their entire learning process by analyzing their historical learning data and predicting their future learning performance. Existing forgetting curve theory based knowledge tracing models only consider the general forgetting caused by time intervals, ignoring the specific influences in KT task. Firstly, the discriminative information in forgetting curve is personalized due to the difference of students. Secondly, the relationship between knowledge concepts could contribute to the generalized features in the forgetting process. Considering these two aspects, we propose a C oncept-driven P ersonalized F orgetting knowledge tracing model (CPF) which integrates the relationships between knowledge concepts and the personalization of students in cognitive abilities. First, personalized cognitive abilities are integrated into the learning and forgetting processes. Individual cognitive differences are modeled to dynamically adjust learning gains and forgetting rates based on students’ knowledge mastery and learning strategies, which enables a more personalized learning experience. Second, the hierarchical relationships among knowledge concepts are considered by designing a precursor-successor knowledge concept matrix. In this way, the potential impact of forgetting prior knowledge concepts on subsequent ones is also integrated in KT task. Furthermore, the proposed personalized forgetting mechanism not only could be applied into the learning of specific knowledge concepts but also in the forgetting-review mechanism of life-long learning process. Extensive experimental results on several public datasets show that our CPF outperforms current forgetting curve theory based methods in predicting student performance, demonstrating CPF can better simulate changes in students’ knowledge state through the personalized forgetting mechanism. Our code is publicly available at https://github.com/lqr-1169/CPF.

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JBHI Journal 2026 Journal Article

SegTom: A 3D Volumetric Medical Image Segmentation Framework for Thoracoabdominal Multi-Organ Anatomical Structures

Yan Pang
Yunhao Li
Jiaming Liang
Hao Chen
Ying Hu
Qiong Wang

Accurate segmentation of thoracoabdominal anatomical structures in three-dimensional medical imaging modalities is fundamental for informed clinical decision-making across a wide array of medical disciplines. Current approaches often struggle to efficiently and comprehensively process this region’s intricate and heterogeneous anatomical information, leading to suboptimal outcomes in diagnosis, treatment planning, and disease management. To address this challenge, we introduce SegTom, a novel volumetric segmentation framework equipped with a cutting-edge SegTom Block specifically engineered to effectively capture the complex anatomical representations inherent to the thoracoabdominal region. This SegTom Block incorporates a hierarchical anatomical-representation decomposition to facilitate efficient information exchange by decomposing the computationally intensive self-attention mechanism and cost-effectively aggregating the extracted representations. Rigorous validation of SegTom across nine diverse datasets, encompassing both computed tomography (CT) and magnetic resonance imaging (MRI) modalities, consistently demonstrates high performance across a broad spectrum of anatomical structures. Specifically, SegTom achieves a mean Dice similarity coefficient (DSC) of 87. 29% for cardiac segmentation on the MM-WHS MRI dataset, 83. 48% for multi-organ segmentation on the BTCV abdominal CT dataset, and 92. 01% for airway segmentation on a dedicated CT dataset.

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JBHI Journal 2025 Journal Article

Cascaded Inner-Outer Clip Retformer for Ultrasound Video Object Segmentation

Jialu Li
Lei Zhu
Zhaohu Xing
Baoliang Zhao
Ying Hu
Faqin Lv
Qiong Wang

Computer-aided ultrasound (US) imaging is an important prerequisite for early clinical diagnosis and treatment. Due to the harsh ultrasound (US) image quality and the blurry tumor area, recent memory-based video object segmentation models (VOS) achieve frame-level segmentation by performing intensive similarity matching among the past frames which could inevitably result in computational redundancy. In this paper, we first build a larger annotated benchmark dataset for breast lesion segmentation in ultrasound videos, then we propose a lightweight clip-level VOS framework for achieving higher segmentation accuracy while maintaining the speed. Then an Inner-Outer Clip Retformer is proposed to extract spatial-temporal tumor features in parallel. Specifically, the proposed Outer Clip Retformer extracts the tumor movement feature from past video clips to locate the current clip tumor position, while the Inner Clip Retformer detailedly extracts current tumor features that can produce more accurate segmentation results. Then a Clip Contrastive loss function is further proposed to align the extracted tumor features along both the spatial-temporal dimensions to improve the segmentation accuracy. In addition, the Global Retentive Memory is proposed to maintain the complementary tumor features with lower computing resources which can generate coherent temporal movement features. In this way, our model can significantly improve the spatial-temporal perception ability without increasing a large number of parameters, achieving more accurate segmentation results while maintaining a faster segmentation speed. Finally, we conduct extensive experiments to evaluate our proposed model on several video object segmentation datasets, the results show that our framework outperforms state-of-the-art segmentation methods.

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JBHI Journal 2025 Journal Article

Efficient Breast Lesion Segmentation From Ultrasound Videos Across Multiple Source-Limited Platforms

Yan Pang
Yunhao Li
Teng Huang
Jiaming Liang
Ziyu Ding
Hao Chen
Baoliang Zhao
Ying Hu

Medical video segmentation is fundamentally important in clinical diagnosis and treatment procedures, offering dynamic tracking of breast lesions across frames in ultrasound videos for improved segmentation performance. However, existing approaches face challenges in striking a balance between segmentation performance and inference speed, hindering real-time application in resource-constrained medical environments. In order to address these limitations, we present BaS, a blazing-fast on-device breast lesion segmentation model. BaS integrates the Stem module and BaSBlock to refine representations through inter- and intra-frame analysis on ultrasound videos. In addition, we release two versions of BaS: the BaS-S for superior segmentation performance and the BaS-L for accelerated inference times. Experimental Results indicate that BaS surpasses the top-performing models in terms of segmenting efficiency and accuracy of predictions on devices with limited resources. This work advances the development of efficient medical video segmentation frameworks applicable to multiple medical platforms.

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AAAI Conference 2025 Conference Paper

GDiffRetro: Retrosynthesis Prediction with Dual Graph Enhanced Molecular Representation and Diffusion Generation

Shengyin Sun
Wenhao Yu
Yuxiang Ren
Weitao Du
Liwei Liu
Xuecang Zhang
Ying Hu
Chen Ma

Retrosynthesis prediction focuses on identifying reactants capable of synthesizing a target product. Typically, the retrosynthesis prediction involves two phases: Reaction Center Identification and Reactant Generation. However, we argue that most existing methods suffer from two limitations in the two phases: (i) Existing models do not adequately capture the ``face'' information in molecular graphs for the reaction center identification. (ii) Current approaches for the reactant generation predominantly use sequence generation in a 2D space, which lacks versatility in generating reasonable distributions for completed reactive groups and overlooks molecules' inherent 3D properties. To overcome the above limitations, we propose GDiffRetro. For the reaction center identification, GDiffRetro uniquely integrates the original graph with its corresponding dual graph to represent molecular structures, which helps guide the model to focus more on the faces in the graph. For the reactant generation, GDiffRetro employs a conditional diffusion model in 3D to further transform the obtained synthon into a complete reactant. Our experimental findings reveal that GDiffRetro outperforms state-of-the-art semi-template models across various evaluative metrics.

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YNIMG Journal 2025 Journal Article

Neuroanatomical and functional substrates of the short video addiction and its association with brain transcriptomic and cellular architecture

Yuanyuan Gao
Ying Hu
Jinlian Wang
Chang Liu
Hohjin Im
Weipeng Jin
Wenwei Zhu
Wei Ge

Short video addiction (SVA) has emerged as a growing behavioral and social issue, driven by the widespread use of digital platforms that provide highly engaging, personalized, and brief video content. We investigated the neuroanatomical and functional substrates of SVA symptoms, alongside brain transcriptomic and cellular characteristics, using Inter-Subject Representational Similarity Analysis (IS-RSA) and transcriptomic approaches. Behaviorally, we found that dispositional envy was associated with SVA. Structurally, SVA was positively correlated with increased morphological volumes in the orbitofrontal cortex (OFC) and bilateral cerebellum. Functionally, the dorsolateral prefrontal cortex (DLPFC), posterior cingulate cortex (PCC), cerebellum, and temporal pole (TP) exhibited heightened spontaneous activity, which was positively correlated with SVA severity. Transcriptomic and cellular analyses also showed specific genes linked to gray matter volume (GMV) associated with SVA, with predominant expression in excitatory and inhibitory neurons. These genes showed distinct spatiotemporal expression patterns in the cerebellum during adolescence. This study offers a comprehensive framework integrating structural, functional, and neurochemical evidence to highlight the neural-transcriptomic underpinnings of SVA symptoms in a non-clinical population.

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JBHI Journal 2025 Journal Article

Online Self-Distillation and Self-Modeling for 3D Brain Tumor Segmentation

Yan Pang
Yunhao Li
Teng Huang
Jiaming Liang
Zhen Wang
Changyu Dong
Dongyang Kuang
Ying Hu

In the specialized domain of brain tumor segmentation, supervised segmentation approaches are hindered by the limited availability of high-quality labeled data, a condition arising from data privacy concerns, significant costs, and ethical issues. In response to this challenge, this paper presents a training framework that adeptly integrates a plug-and-play component, MOD, into current supervised learning models, boosting their efficacy in scenarios with limited data. The MOD consists of an Online Tokenizer and a Dense Predictor, which employs self-distillation and self-modeling on masked patches, promoting swift convergence and efficient representation learning. During the inference phase, the plug-and-play MOD component is excluded, preserving the computational efficiency of the original model without incurring extra processing costs. We substantiated the value of our approach through experiments on leading 3D brain tumor segmentation baselines. Remarkably, models augmented with the MOD consistently showcased superior results, achieving elevated Dice coefficients and HD95 scores on two datasets: BraTS 2021 and MSD 2019 Task-01 Brain Tumor.

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YNIMG Journal 2025 Journal Article

White matter hyperintensity-associated iron overload links glymphatic system dysfunction to cognitive impairment in cerebral small vessel disease

Yage Qiu
Ying Hu
Weina Ding
Qingyang Fu
Wentao Hu
Yuanzheng Wang
Qun Xu
Yongming Dai

Glymphatic system function has been increasingly linked to cognition in cerebral small vessel disease (CSVD), although the underlying pathological mechanisms related to brain metabolism remain to be fully clarified. Iron overload within white matter hyperintensity (WMH), potentially reflecting metabolic abnormalities, may play a pivotal role in this process. This study investigated whether WMH iron burden mediates the association between glymphatic dysfunction and cognitive impairment in CSVD. A total of 102 patients with CSVD and 29 matched healthy controls (HCs) underwent brain MRI and cognitive assessments. WMH iron burden was quantified using a sub-voxel quantitative approach, while glymphatic function was assessed with the Diffusion Tensor Image Analysis aLong the Perivascular Space (DTI-ALPS) index. Correlation and mediation analyses were then conducted to evaluate relationships among WMH iron burden, DTI-ALPS index, and cognitive scores. Compared with HCs, CSVD patients exhibited significantly higher WMH iron burden, lower DTI-ALPS index, and poorer cognitive performances. Elevated WMH iron burden was associated with deficits in attention-executive (att-exe), memory, and visual-spatial domains, whereas reduced DTI-ALPS index correlated with impaired att-exe and memory function. Importantly, WMH iron burden fully mediated the link between DTI-ALPS index and both att-exe function (p < 0.001) and memory (p = 0.02) in the CSVD group. These findings noninvasively identify WMH iron overload, a probable representative of microglial activation, as a key mediator between glymphatic dysfunction and cognitive decline in CSVD, prompting a potential therapeutic target for disease management.

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YNIMG Journal 2024 Journal Article

Exploring cognitive related microstructural alterations in normal appearing white matter and deep grey matter for small vessel disease: A quantitative susceptibility mapping study

Yawen Sun
Wentao Hu
Ying Hu
Yage Qiu
Yuewei Chen
Qun Xu
Hongjiang Wei
Yongming Dai

Brain microstructural alterations possibly occur in the normal-appearing white matter (NAWM) and grey matter of small vessel disease (SVD) patients, and may contribute to cognitive impairment. The aim of this study was to explore cognitive related microstructural alterations in white matter and deep grey matter nuclei in SVD patients using magnetic resonance (MR) quantitative susceptibility mapping (QSM). 170 SVD patients, including 103 vascular mild cognitive impairment (VaMCI) and 67 no cognitive impairment (NCI), and 21 healthy control (HC) subjects were included, all underwent a whole-brain QSM scanning. Using a white matter and a deep grey matter atlas, subregion-based QSM analysis was conducted to identify and characterize microstructural alterations occurring within white matter and subcortical nuclei. Significantly different susceptibility values were revealed in NAWM and in several specific white matter tracts including anterior limb of internal capsule, corticospinal tract, medial lemniscus, middle frontal blade, superior corona radiata and tapetum among VaMCI, NCI and HC groups. However, no difference was found in white matter hyperintensities between VaMCI and NCI. A trend toward higher susceptibility in the caudate nucleus and globus pallidus of VaMCI patients compared to HC, indicating elevated iron deposition in these areas. Interestingly, some of these QSM parameters were closely correlated with both global and specific cognitive function scores, controlling age, gender and education level. Our study suggested that QSM may serve as a useful imaging tool for monitoring cognitive related microstructural alterations in brain. This is especially meaningful for white matter which previously lacks of attention.

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YNIMG Journal 2024 Journal Article

Happy people are always similar: The evidence from brain morphological and functional inter-subject correlations

Zixi Li
Keying Jiang
Ye Zhu
Hanxiao Du
Hohjin Im
Yingying Zhu
Lei Feng
Wenwei Zhu

A fundamental question in the study of happiness is whether there is neural evidence to support a well-known hypothesis that happy people are always similar while unfortunate people have their own misfortunes. To investigate this, we employed several happiness-related questionnaires to identify potential components of happiness, and further investigated and confirmed their associations with personality, mood, aggressive behaviors, and amygdala reactivity to fearful faces within a substantial sample size of college students (n = 570). Additionally, we examined the functional and morphological similarities and differences among happy individuals using the inter-subject representational similarity analysis (IS-RSA). IS-RSA emphasizes the geometric properties in a high-dimensional space constructed by brain or behavioral patterns and focuses on individual subjects. Our behavioral findings unveiled two factors of happiness: individual and social, both of which mediated the effect of personality traits on individual aggression. Subsequently, mood mediated the impact of happiness on aggressive behaviors across two subgroup splits. Functional imaging data revealed that individuals with higher levels of happiness exhibited reduced amygdala reactivity to fearful faces, as evidenced by a conventional face-matching task (n = 104). Moreover, IS-RSA demonstrated that these participants manifested similar neural activation patterns when processing fearful faces within the visual pathway, but not within the emotional network (e.g., amygdala). Morphological observations (n = 425) indicated that individuals with similar high happiness levels exhibited comparable gray matter volume patterns within several networks, including the default mode network, fronto-parietal network, visual network, and attention network. Collectively, these findings offer early neural evidence supporting the proposition that happy individuals may share common neural characteristics.

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NeurIPS Conference 2024 Conference Paper

Magnet: We Never Know How Text-to-Image Diffusion Models Work, Until We Learn How Vision-Language Models Function

Chenyi Zhuang
Ying Hu
Pan Gao

Text-to-image diffusion models particularly Stable Diffusion, have revolutionized the field of computer vision. However, the synthesis quality often deteriorates when asked to generate images that faithfully represent complex prompts involving multiple attributes and objects. While previous studies suggest that blended text embeddings lead to improper attribute binding, few have explored this in depth. In this work, we critically examine the limitations of the CLIP text encoder in understanding attributes and investigate how this affects diffusion models. We discern a phenomenon of attribute bias in the text space and highlight a contextual issue in padding embeddings that entangle different concepts. We propose Magnet, a novel training-free approach to tackle the attribute binding problem. We introduce positive and negative binding vectors to enhance disentanglement, further with a neighbor strategy to increase accuracy. Extensive experiments show that Magnet significantly improves synthesis quality and binding accuracy with negligible computational cost, enabling the generation of unconventional and unnatural concepts.

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YNIMG Journal 2024 Journal Article

Microstructural and functional substrates underlying dispositional greed and its link with trait but not state impulsivity

Keying Jiang
Jinlian Wang
Yuanyuan Gao
Xiang Li
Hohjin Im
Yingying Zhu
Hanxiao Du
Lei Feng

The interplay between personality traits and impulsivity has long been a central theme in psychology and psychiatry. However, the potential association between Greed Personality Traits (GPT) and impulsivity, encompassing both trait and state impulsivity and future time perspective, remains largely unexplored. To address these issues, we employed questionnaires and an inter-temporal choice task to estimate corresponding trait/state impulsivity and collected multi-modal neuroimaging data (resting-state functional imaging: n = 430; diffusion-weighted imaging: n = 426; task-related functional imaging: n = 53) to investigate the underlying microstructural and functional substrates. Behavioral analyses revealed that GPT mediated the association between time perspective (e.g., present fatalism) and trait impulsivity (e.g., motor impulsivity). Functional imaging analyses further identified that brain activation strengths and patterns related to delay length, particularly in the dorsomedial prefrontal cortex, superior parietal lobule, and cerebellum, were associated with GPT. Moreover, individuals with similar levels of greed exhibited analogous spontaneous brain activity patterns, predominantly in the Default Mode Network (DMN), Fronto-Parietal Network (FPN), and Visual Network (VIS). Diffusion imaging analysis observed specific microstructural characteristics in the spinocerebellar/pontocerebellar fasciculus, internal/external capsule, and corona radiata that support the formation of GPT. Furthermore, the corresponding neural activation pattern, spontaneous neural activity pattern, and analogous functional couplings among the aforementioned brain regions mediated the relationships between time perspective and GPT and between GPT and motor impulsivity. These findings provide novel insights into the possible pathway such as time perspective → dispositional greed → impulsivity and uncover their underlying microstructural and functional substrates.

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NeurIPS Conference 2024 Conference Paper

Uni-Med: A Unified Medical Generalist Foundation Model For Multi-Task Learning Via Connector-MoE

Xun Zhu
Ying Hu
Fanbin Mo
Miao Li
Ji Wu

Multi-modal large language models (MLLMs) have shown impressive capabilities as a general-purpose interface for various visual and linguistic tasks. However, building a unified MLLM for multi-task learning in the medical field remains a thorny challenge. To mitigate the tug-of-war problem of multi-modal multi-task optimization in MLLMs, recent advances primarily focus on improving the LLM components, while neglecting the connector that bridges the gap between modalities. In this paper, we introduce Uni-Med, a novel medical generalist foundation model which consists of a universal visual feature extraction module, a connector mixture-of-experts (CMoE) module, and an LLM. Benefiting from the proposed CMoE that leverages a well-designed router with a mixture of projection experts at the connector, Uni-Med achieves efficient solution to the tug-of-war problem and can perform six different medical tasks including question answering, visual question answering, report generation, referring expression comprehension, referring expression generation and image classification. To the best of our knowledge, Uni-Med is the first effort to tackle multi-task interference at the connector in MLLMs. Extensive ablation experiments validate the effectiveness of introducing CMoE under any configuration, with up to an average 8% performance gains. We further provide interpretation analysis of the tug-of-war problem from the perspective of gradient optimization and parameter statistics. Compared to previous state-of-the-art medical MLLMs, Uni-Med achieves competitive or superior evaluation metrics on diverse tasks. Code and resources are available at https: //github. com/MSIIP/Uni-Med.

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IS Journal 2020 Journal Article

Recognizing Nested Named Entity Based on the Neural Network Boundary Assembling Model

Yanping Chen
Yuefei Wu
Yongbin Qin
Ying Hu
Zeyu Wang
Ruizhang Huang
Xinyu Cheng
Ping Chen

The task to recognize named entities is often modeled as a sequence labeling process, which selects a label path whose probability is maximum for an input sentence. Because it makes the assumption that the input sentence has a flattened structure, it often fails to recognize nested named entities. In our previous work, a boundary assembling (BA) model was proposed. It is a cascading framework, which identifies named entity boundaries first, and then assembles them into entity candidates for further assessment. This model is effective to recognize nested named entities, but still suffers from poor performance caused by the sparse feature problem. In this article, the BA model is remodeled with the advancement of neural networks, which enables the model to capture semantic information of a sentence by using word embeddings pretrained in external resources. In our experiments, it shows an impressive improvement on the final performance, outperforming the state of the art more than 17% in F-score.

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AIIM Journal 2020 Journal Article

State recognition of decompressive laminectomy with multiple information in robot-assisted surgery

Yu Sun
Li Wang
Zhongliang Jiang
Bing Li
Ying Hu
Wei Tian

The decompressive laminectomy is a common operation for treatment of lumbar spinal stenosis. The tools for grinding and drilling are used for fenestration and internal fixation, respectively. The state recognition is one of the main technologies in robot-assisted surgery, especially in tele-surgery, because surgeons have limited perception during remote-controlled robot-assisted surgery. The novelty of this paper is that a state recognition system is proposed for the robot-assisted tele-surgery. By combining the learning methods and traditional methods, the robot from the slave-end can think about the current operation state like a surgeon, and provide more information and decision suggestions to the master-end surgeon, which aids surgeons work safer in tele-surgery. For the fenestration, we propose an image-based state recognition method that consists a U-Net derived network, grayscale redistribution and dynamic receptive field assisting in controlling the grinding process to prevent the grinding-bit from crossing the inner edge of the lamina to damage the spinal nerves. For the internal fixation, we propose an audio and force-based state recognition method that consists signal features extraction methods, LSTM-based prediction and information fusion assisting in monitoring the drilling process to prevent the drilling-bit from crossing the outer edge of the vertebral pedicle to damage the spinal nerves. Several experiments are conducted to show the reliability of the proposed system in robot-assisted surgery.

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