Author name cluster

Yihao Guo

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5 papers

1 author row

EAAI Journal 2026 Journal Article

Machine learning to enhance strain-resilience humidity sensing on flexible surface acoustic wave platform

Yanhong Xia
Zhangbin Ji
Jian Zhou
Yihao Guo
Hui Chen
Jinbo Zhang
Yongqing Fu

Flexible surface acoustic wave (SAW) humidity sensors have garnered considerable attention in fields such as environmental monitoring and healthcare, mainly attributed to their advantages such as wearability, applicability in non-planar scenarios, quasi-digital output, and wireless passive capabilities. However, improvement in performance of these flexible SAW humidity sensors faces great challenges such as low electromechanical coupling coefficient, poor humidity response or sensitivity, and introduction of detection errors caused by mechanical strain interference. Herein, we developed a flexible SAW humidity sensor utilizing an aluminum scandium nitride (AlScN) piezoelectric film deposited on ultrathin glass substrates, incorporating ternary nanocomposites of graphene quantum dots-polyethyleneimine-silica nanoparticles (GQDs-PEI-SiO2 NPs) as the sensitive layers, which demonstrated an ultra-high sensitivity of 5. 02 kHz (kHz)/%Relative Humidity (RH). To address critical issues of strain interferences under randomly bending or deformation conditions, we applied machine learning (ML) algorithms to establish correlations between sensor's response signal features and humidity labels, thereby effectively mitigating unreliable humidity measurements caused by significant strain interferences, with improved precision and specificity. After comprehensive evaluation and analysis using various artificial intelligence algorithms, multilayer perceptron regression model was identified as the best performer in humidity prediction under strain interferences, with a coefficient of determination as high as 0. 997 and a mean square error of ∼0. 479. Reliability and generalization capabilities of this model were verified, and such the strategy not only significantly enhances the performance metrics of flexible humidity sensors but also provides an innovative and precision solution under various strain interferences using the flexible SAW sensors.

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IJCAI Conference 2025 Conference Paper

Volumetric Axial Disentanglement Enabling Advancing in Medical Image Segmentation

Xingru Huang
Jian Huang
Yihao Guo
Tianyun Zhang
Zhao Huang
Yaqi Wang
Ruipu Tang
Guangliang Cheng

Information retrieved from three dimensions is treated uniformly in CNN-based volumetric segmentation methods. However, such neglect of axial disparities fails to capture true spatio-temporal variations. This paper introduces the volumetric axial disentanglement to address the disparities in spatial information along different axial dimensions. Building on this concept, we propose the Post-Axial Refiner (PaR) module to refine segmentation masks by implementing axial disentanglement on the specific axis of the volumetric medical sequences. As a plug-and-play enhancement to existing volumetric segmentation architecture, PaR further utilizes specialized attention approaches to learn disentangled post-decoding features, enhancing spatial representation and structural detail. Validation on various datasets demonstrates PaR's consistent elevation of segmentation precision and boundary clarity across 11 baselines and different imaging modalities, achieving state-of-the-art performance on multiple datasets. Experimental tests demonstrate the ability of volumetric axial disentanglement to refine the segmentation of volumetric medical images. Code is released at https: //github. com/IMOP-lab/PaR-Pytorch.

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NeurIPS Conference 2024 Conference Paper

Upping the Game: How 2D U-Net Skip Connections Flip 3D Segmentation

Xingru Huang
Yihao Guo
Jian Huang
Tianyun Zhang
Hong He
Shaowei Jiang
Yaoqi Sun

In the present study, we introduce an innovative structure for 3D medical image segmentation that effectively integrates 2D U-Net-derived skip connections into the architecture of 3D convolutional neural networks (3D CNNs). Conventional 3D segmentation techniques predominantly depend on isotropic 3D convolutions for the extraction of volumetric features, which frequently engenders inefficiencies due to the varying information density across the three orthogonal axes in medical imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI). This disparity leads to a decline in axial-slice plane feature extraction efficiency, with slice plane features being comparatively underutilized relative to features in the time-axial. To address this issue, we introduce the U-shaped Connection (uC), utilizing simplified 2D U-Net in place of standard skip connections to augment the extraction of the axial-slice plane features while concurrently preserving the volumetric context afforded by 3D convolutions. Based on uC, we further present uC 3DU-Net, an enhanced 3D U-Net backbone that integrates the uC approach to facilitate optimal axial-slice plane feature utilization. Through rigorous experimental validation on five publicly accessible datasets—FLARE2021, OIMHS, FeTA2021, AbdomenCT-1K, and BTCV, the proposed method surpasses contemporary state-of-the-art models. Notably, this performance is achieved while reducing the number of parameters and computational complexity. This investigation underscores the efficacy of incorporating 2D convolutions within the framework of 3D CNNs to overcome the intrinsic limitations of volumetric segmentation, thereby potentially expanding the frontiers of medical image analysis. Our implementation is available at https: //github. com/IMOP-lab/U-Shaped-Connection.

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YNICL Journal 2021 Journal Article

Brain iron assessment in patients with First-episode schizophrenia using quantitative susceptibility mapping

Man Xu
Yihao Guo
Junying Cheng
Kangkang Xue
Meng Yang
Xueqin Song
Yanqiu Feng
Jingliang Cheng

PURPOSE: Decreased serum ferritin level was recently found in schizophrenia. Whether the brain iron concentration in schizophrenia exists abnormality is of research significance. Quantitative susceptibility mapping (QSM) was used in this study to assess brain iron changes in the grey matter nuclei of patients with first-episode schizophrenia. METHODS: * was evaluated using receiver operating characteristic curve. The correlations between regional iron variations and clinical PANSS (Positive and Negative Syndrome Scale) scores were assessed using partial correlation analysis. RESULTS: * values did not show significant correlations with PANSS scores (p > 0.05). CONCLUSION: * in the evaluation of schizophrenia-related brain iron changes. It demonstrated that QSM may be a potential biomarker for further understanding the pathophysiological mechanism of first-episode schizophrenia.

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YNICL Journal 2017 Journal Article

Alterations of white matter structural networks in patients with non-neuropsychiatric systemic lupus erythematosus identified by probabilistic tractography and connectivity-based analyses

Man Xu
Xiangliang Tan
Xinyuan Zhang
Yihao Guo
Yingjie Mei
Qianjin Feng
Yikai Xu
Yanqiu Feng

PURPOSE: Systemic lupus erythematosus (SLE) is a chronic inflammatory female-predominant autoimmune disease that can affect the central nervous system and exhibit neuropsychiatric symptoms. In SLE patients without neuropsychiatric symptoms (non-NPSLE), recent diffusion tensor imaging studies showed white matter abnormalities in their brains. The present study investigated the entire brain white matter structural connectivity in non-NPSLE patients by using probabilistic tractography and connectivity-based analyses. METHODS: Whole-brain structural networks of 29 non-NPSLE patients and 29 healthy controls (HCs) were examined. The structural networks were constructed with interregional probabilistic connectivity. Graph theory analysis was performed to investigate the topological properties, and network-based statistic was employed to assess the alterations of the interregional connections among non-NPSLE patients and controls. RESULTS: Compared with HCs, non-NPSLE patients demonstrated significantly decreased global and local network efficiencies and showed increased characteristic path length. This finding suggests that the global integration and local specialization were impaired. Moreover, the regional properties (nodal efficiency and degree) in the frontal, occipital, and cingulum regions of the non-NPSLE patients were significantly changed and negatively correlated with the disease activity index. The distribution pattern of the hubs measured by nodal degree was altered in the patient group. Finally, the non-NPSLE group exhibited decreased structural connectivity in the left median cingulate-centered component and increased connectivity in the left precuneus-centered component and right middle temporal lobe-centered component. CONCLUSION: This study reveals an altered topological organization of white matter networks in non-NPSLE patients. Furthermore, this research provides new insights into the structural disruptions underlying the functional and neurocognitive deficits in non-NPSLE patients.

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