Author name cluster

Ying Yuan

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9 papers

2 author rows

EAAI Journal 2026 Journal Article

Improving cell localization with attention-guided diffusion models

Wei Liu
Chao Xu
Ying Yuan
Wenqi Ye
Huan Xiong
Wenqiao Qiu
Lili Guo
Xinda Li

Cell localization plays a crucial role in pathology image analysis and is traditionally accomplished through density map regression. However, the use of broad Gaussian kernels in generating ground truth density maps often leads to susceptibility to background noise, resulting in density loss. Narrowing the Gaussian kernel could alleviate this issue, but current methods struggle with density maps generated using narrow kernels. To address this challenge, the diffusion model presents a viable solution by modeling complex distributions and maintaining stability during density map training. In this work, we explore the use of the diffusion model to recover density maps from fully Gaussian noise and present the first Diffusion model for Cell localization. Additionally, we design an Attention map Guidance mechanism that enables the diffusion model to generate higher-quality samples with a moderate guidance scale. Given the noise present at intermediate steps of the diffusion process, we also incorporate a regression branch to estimate cell counts during training. We conduct extensive experiments on several public datasets to validate the effectiveness of the proposed method. The experimental results demonstrate that the proposed method achieves notable improvements in localization and counting performance across multiple datasets.

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IROS Conference 2025 Conference Paper

Generalizable Humanoid Manipulation with 3D Diffusion Policies

Yanjie Ze
Zixuan Chen
Wenhao Wang
Tianyi Chen
Xialin He
Ying Yuan
Xue Bin Peng
Jiajun Wu 0001

Humanoid robots capable of autonomous operation in diverse environments have long been a goal for roboticists. However, autonomous manipulation by humanoid robots has largely been restricted to one specific scene, primarily due to the difficulty of acquiring generalizable skills and the expensiveness of in-the-wild humanoid robot data. In this work, we build a real-world robotic system to address this challenging problem. Our system is mainly an integration of 1) a whole-upper-body robotic teleoperation system to acquire human-like robot data, 2) a 25-DoF humanoid robot platform with a height-adjustable cart and a 3D LiDAR sensor, and 3) an improved 3D Diffusion Policy learning algorithm for humanoid robots to learn from noisy human data. We run more than 2000 episodes of policy rollouts on the real robot for rigorous policy evaluation. Empowered by this system, we show that using only data collected in one single scene and with only onboard computing, a full-sized humanoid robot can autonomously perform skills in diverse real-world scenarios. Videos are available at humanoid-manipulation.github.io.

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EAAI Journal 2024 Journal Article

A traffic-weather generative adversarial network for traffic flow prediction for road networks under bad weather

Wensong Zhang
Ronghan Yao
Ying Yuan
Xiaojing Du
Libing Wang
Feng Sun

Traffic flow prediction is pivotal in providing reliable information for intelligent traffic systems. Unexpected events, such as bad weather, unavoidably impact the precision of traffic flow prediction. Therefore, to achieve accurate traffic flow prediction results in road networks under bad weather, a novel Traffic-Weather Generative Adversarial Network (TWeather-GAN model) is developed. This model comprises a Generator and a Discriminator. The Generator incorporates both the traffic and weather modules to extract the spatiotemporal patterns hidden in traffic flow and weather data. In the traffic and weather modules, the gated convolutional layer, Encoder-Decoder architecture, and attention mechanism are established. In the Discriminator, the gated convolutional layer and bidirectional long short-term memory neural network are introduced. Traffic flow data under fog, strong wind, and heavy rain are selected to test the seven baseline models and the proposed model, and the ablation experiments are conducted to analyze the mechanism of the proposed model. The experiments demonstrate that the TWeather-GAN model outperforms the baseline models under bad weather, and makes the prediction error have an average reduction of 0. 20%–34. 81%, 3. 21%–35. 22%, and 9. 46%–39. 10%, respectively, under one-step prediction, three-step prediction, and six-step prediction. Furthermore, establishing the gated convolutional layer and the weather module enhances the accuracy of traffic flow prediction under bad weather. Results show that traffic flow fluctuations and distributions differ under fog and strong wind, and heavy rain affects the trend of traffic flow over one day.

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EAAI Journal 2024 Journal Article

An improved medical image segmentation framework with Channel-Height-Width-Spatial attention module

Xiang Yu
Hongbo Guo
Ying Yuan
Wenjia Guo
Xia Yang
Hui Xu
Yanqing Kong
Yudong Zhang

This paper presents an improved version of the U-Net segmentation framework for medical image segmentation, called CHWS-UNet. To build the proposed framework CHWS-UNet, we first develop a novel lightweight channel attention module called LCAM, based on which we further propose the Channel-Height-Width-Spatial (CHWS) attention module for channel, height, width, and spatial dimension-level feature refinement. Our CHWS-UNet is constructed by integrating the proposed CHWS attention modules into the shortcut paths between the encoder and the decoder stem. To justify the effectiveness of the proposed modules and networks, we then carried out extensive experiments on four public medical image datasets, including BUSI, ISIC2017, ISIC2018, PH and a proprietary uterus lesion ultrasound dataset from Shenzhen Maternity and Child Healthcare Hospital. The results show that the proposed attention module can significantly improve the performance of baseline models, even on small medical image datasets, without introducing noticeable parameters and computational costs. Further, the proposed segmentation framework can achieve promising performance compared to edge-cutting frameworks. The code can be found at CHWS-UNet.

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IROS Conference 2024 Conference Paper

Robot Generating Data for Learning Generalizable Visual Robotic Manipulation

Yunfei Li
Ying Yuan
Jingzhi Cui
Haoran Huan
Wei Fu
Jiaxuan Gao
Zekai Xu
Yi Wu

It has been a popular trend in AI to pretrain foundation models on massive data. However, collecting sufficient offline training trajectories for robot learning is particularly expensive since valid control actions are required. Therefore, most existing robotic datasets are collected from human experts. We tackle such a data collection issue with a new framework called "robot self-teaching", which asks the robot to self-generate effective training data instead of relying on human demonstrators. Our key idea is to train a separate data-generation policy operating on the state space to automatically generate meaningful actions and trajectories with ever-growing complexities. Then, these generated data can be further used to train a visual policy with strong compositional generalization capabilities. We validate our framework in two visual manipulation testbeds, including a multi-object stacking domain and a popular RL benchmark "Franka kitchen". Experiments show that the final visual policy trained on self-generated data can accomplish novel testing goals that require long-horizon robot executions. Project website https://sites.google.com/view/robot-self-teaching.

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ICRA Conference 2024 Conference Paper

Robot Synesthesia: In-Hand Manipulation with Visuotactile Sensing

Ying Yuan
Haichuan Che
Yuzhe Qin
Binghao Huang
Zhao-Heng Yin
Kang-Won Lee
Yi Wu 0013
Soo-Chul Lim

Executing contact-rich manipulation tasks necessitates the fusion of tactile and visual feedback. However, the distinct nature of these modalities poses significant challenges. In this paper, we introduce a system that leverages visual and tactile sensory inputs to enable dexterous in-hand manipulation. Specifically, we propose Robot Synesthesia, a novel point cloudbased tactile representation inspired by human tactile-visual synesthesia. This approach allows for the simultaneous and seamless integration of both sensory inputs, offering richer spatial information and facilitating better reasoning about robot actions. Comprehensive ablations are performed on how the integration of vision and touch can improve reinforcement learning and Sim2Real performance. Our project page is available at https://yingyuan0414.github.io/visuotactile/.

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YNICL Journal 2021 Journal Article

Hippocampal subfield and anterior-posterior segment volumes in patients with sporadic amyotrophic lateral sclerosis

Shuangwu Liu
Qingguo Ren
Gaolang Gong
Yuan Sun
Bing Zhao
Xiaotian Ma
Na Zhang
Suyu Zhong

Neuroimaging studies of hippocampal volumes in patients with amyotrophic lateral sclerosis (ALS) have reported inconsistent results. Our aims were to demonstrate that such discrepancies are largely due to atrophy of different regions of the hippocampus that emerge in different disease stages of ALS and to explore the existence of co-pathology in ALS patients. We used the well-validated King's clinical staging system for ALS to classify patients into different disease stages. We investigated in vivo hippocampal atrophy patterns across subfields and anterior-posterior segments in different King's stages using structural MRI in 76 ALS patients and 94 health controls (HCs). The thalamus, corticostriatal tract and perforant path were used as structural controls to compare the sequence of alterations between these structures and the hippocampal subfields. Compared with HCs, ALS patients at King's stage 1 had lower volumes in the bilateral posterior subiculum and presubiculum; ALS patients at King's stage 2 exhibited lower volumes in the bilateral posterior subiculum, left anterior presubiculum and left global hippocampus; ALS patients at King's stage 3 showed significantly lower volumes in the bilateral posterior subiculum, dentate gyrus and global hippocampus. Thalamic atrophy emerged at King's stage 3. White matter tracts remained normal in a subset of ALS patients. Our study demonstrated that the pattern of hippocampal atrophy in ALS patients varies greatly across King's stages. Future studies in ALS patients that focus on the hippocampus may help to further clarify possible co-pathologies in ALS.

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YNIMG Journal 2017 Journal Article

Genome-wide association analysis of secondary imaging phenotypes from the Alzheimer's disease neuroimaging initiative study

Wensheng Zhu
Ying Yuan
Jingwen Zhang
Fan Zhou
Rebecca C. Knickmeyer
Hongtu Zhu

The aim of this paper is to systematically evaluate a biased sampling issue associated with genome-wide association analysis (GWAS) of imaging phenotypes for most imaging genetic studies, including the Alzheimer's Disease Neuroimaging Initiative (ADNI). Specifically, the original sampling scheme of these imaging genetic studies is primarily the retrospective case-control design, whereas most existing statistical analyses of these studies ignore such sampling scheme by directly correlating imaging phenotypes (called the secondary traits) with genotype. Although it has been well documented in genetic epidemiology that ignoring the case-control sampling scheme can produce highly biased estimates, and subsequently lead to misleading results and suspicious associations, such findings are not well documented in imaging genetics. We use extensive simulations and a large-scale imaging genetic data analysis of the Alzheimer's Disease Neuroimaging Initiative (ADNI) data to evaluate the effects of the case-control sampling scheme on GWAS results based on some standard statistical methods, such as linear regression methods, while comparing it with several advanced statistical methods that appropriately adjust for the case-control sampling scheme.

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YNIMG Journal 2014 Journal Article

FMEM: Functional mixed effects modeling for the analysis of longitudinal white matter Tract data

Ying Yuan
John H. Gilmore
Xiujuan Geng
Styner Martin
Kehui Chen
Jane-Ling Wang
Hongtu Zhu

Many longitudinal imaging studies have collected repeated diffusion tensor magnetic resonance imaging data to understand white matter maturation and structural connectivity pattern in normal controls and diseased subjects. There is an urgent demand for the development of statistical methods for the analysis of diffusion properties along fiber tracts and clinical data obtained from longitudinal studies. Jointly analyzing repeated fiber-tract diffusion properties and covariates (e. g. , age or gender) raises several major challenges including (i) infinite-dimensional functional response data, (ii) complex spatial–temporal correlation structure, and (iii) complex spatial smoothness. To address these challenges, this article is to develop a functional mixed effects modeling (FMEM) framework to delineate the dynamic changes of diffusion properties along major fiber tracts and their association with a set of covariates of interest and the structure of the variability of these white matter tract properties in various longitudinal studies. Our FMEM consists of a functional mixed effects model for addressing all three challenges, an efficient method for spatially smoothing varying coefficient functions, an estimation method for estimating the spatial–temporal correlation structure, a test procedure with local and global test statistics for testing hypotheses of interest associated with functional response, and a simultaneous confidence band for quantifying the uncertainty in the estimated coefficient functions. Simulated data are used to evaluate the finite sample performance of FMEM and to demonstrate that FMEM significantly outperforms the standard pointwise mixed effects modeling approach. We apply FMEM to study the spatial–temporal dynamics of white-matter fiber tracts in a clinical study of neurodevelopment.

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