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Lin Guo

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8 papers

2 author rows

JBHI Journal 2026 Journal Article

GATE: Graph and Text Exchange for Zero-Shot ECG Classification with LLM Prompts

Ying An
Shiyu Tang
Xianlai Chen
Lin Guo

Electrocardiography (ECG) is a fundamental tool for diagnosing cardiovascular diseases, yet the scarcity of large-scale annotated data limits the applicability of supervised learning approaches. While self-supervised learning (SSL) has shown promise for ECG representation learning, existing methods often suffer from semantic distortion, insufficient spatial modeling, and a lack of integration with medical knowledge. To address these challenges, we propose GATE (Graph-And-Text Exchange), a novel multimodal SSL framework that enhances the quality of the representation of ECG through cross-modal exchange between graph-structured data and clinical ECG reports. GATE employs a spatiotemporal graph encoder to capture fine-grained intra- and inter-lead dependencies, and introduces a lexical knowledge-embedded codebook to enhance the semantic representation of clinical reports, facilitating effective graph-text alignment. During inference, GATE integrates a large language model with a domain-specific knowledge base to generate semantically enriched disease descriptions, enabling robust zero-shot classification. Extensive experiments on three real-world ECG datasets demonstrate that GATE outperforms state-of-the-art self-supervised and multimodal baselines under both low-resource and zero-shot settings. Notably, GATE achieves competitive performance even when trained on only 1% of labeled data, highlighting its strong generalization and clinical potential.

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JBHI Journal 2025 Journal Article

Improvement of Non-Invasive Glucose Estimation Accuracy Through Multi-Wavelength PPG

Taixiang Li
Quangui Wang
Linghao Lei
Ying An
Lin Guo
Linan Ren
Xianlai Chen

Effective diabetes management requires regular and accurate blood glucose monitoring; however, traditional invasive methods often cause discomfort and inconvenience. Non-invasive techniques such as photoplethysmography (PPG) have been explored, though single-wavelength PPG systems are limited by the overlapping absorption characteristics between glucose and other biological components, such as water and fat. In this study, a novel multi-wavelength PPG system integrated with temperature and humidity sensors is introduced, coupled with a neural network framework featuring attention mechanisms to enhance glucose prediction. The system employs six optical sensors covering wavelengths from the visible to near-infrared (NIR) spectrum, enabling deeper tissue penetration and enhanced glucose specificity by targeting distinct absorption peaks—especially those above 1000 nm. The system was validated using a robust dataset of 26, 063 measurements from 254 participants. The experimental results demonstrate significant improvements, with the model achieving 86. 49% compliance with the ISO 15197: 2013 standards and 91. 80% of measurements falling within Zone A of the Parkes error grid. The introduction of multiple wavelengths clearly improves performance over single-wavelength systems, and wavelengths above 1000 nm were shown to have a higher contribution in glucose prediction. In addition, the incorporation of temperature and humidity data also enhanced performance by accounting for environmental and physiological factors, and that demographic and meal-related factors significantly impact prediction accuracy, thereby underscoring the potential of this system as a reliable, non-invasive, and personalized glucose monitoring tool.

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JBHI Journal 2025 Journal Article

Multi-Scale Group Agent Attention-Based Graph Convolutional Decoding Networks for 2D Medical Image Segmentation

Zhichao Wang
Lin Guo
Shuchang Zhao
Shiqing Zhang
Xiaoming Zhao
Jiangxiong Fang
Guoyu Wang
Hongsheng Lu

Automated medical image segmentation plays a crucial role in assisting doctors in diagnosing diseases. Feature decoding is a critical yet challenging issue for medical image segmentation. To address this issue, this work proposes a novel feature decoding network, called multi-scale group agent attention-based graph convolutional decoding networks (MSGAA-GCDN), to learn local-global features in graph structures for 2D medical image segmentation. The proposed MSGAA-GCDN combines graph convolutional network (GCN) and a lightweight multi-scale group agent attention (MSGAA) mechanism to represent features globally and locally within a graph structure. Moreover, in skip connections a simple yet efficient attention-based upsampling convolution fusion (AUCF) module is designed to enhance encoder-decoder feature fusion in both channel and spatial dimensions. Extensive experiments are conducted on three typical medical image segmentation tasks, namely Synapse abdominal multi-organs, Cardiac organs, and Polyp lesions. Experimental results demonstrate that the proposed MSGAA-GCDN outperforms the state-of-the-art methods, and the designed MSGAA is a lightweight yet effective attention architecture. The proposed MSGAA-GCDN can be easily taken as a plug-and-play decoder cascaded with other encoders for general medical image segmentation tasks.

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NeurIPS Conference 2025 Conference Paper

Revisiting Generative Infrared and Visible Image Fusion Based on Human Cognitive Laws

Lin Guo
Xiaoqing Luo
Wei Xie
Zhancheng Zhang
Hui Li
Rui Wang
Zhenhua Feng
Xiaoning Song

Existing infrared and visible image fusion methods often face the dilemma of balancing modal information. Generative fusion methods reconstruct fused images by learning from data distributions, but their generative capabilities remain limited. Moreover, the lack of interpretability in modal information selection further affects the reliability and consistency of fusion results in complex scenarios. This manuscript revisits the essence of generative image fusion under the inspiration of human cognitive laws and proposes a novel infrared and visible image fusion method, termed HCLFuse. First, HCLFuse investigates the quantification theory of information mapping in unsupervised fusion networks, which leads to the design of a multi-scale mask-regulated variational bottleneck encoder. This encoder applies posterior probability modeling and information decomposition to extract accurate and concise low-level modal information, thereby supporting the generation of high-fidelity structural details. Furthermore, the probabilistic generative capability of the diffusion model is integrated with physical laws, forming a time-varying physical guidance mechanism that adaptively regulates the generation process at different stages, thereby enhancing the ability of the model to perceive the intrinsic structure of data and reducing dependence on data quality. Experimental results show that the proposed method achieves state-of-the-art fusion performance in qualitative and quantitative evaluations across multiple datasets and significantly improves semantic segmentation metrics. This fully demonstrates the advantages of this generative image fusion method, drawing inspiration from human cognition, in enhancing structural consistency and detail quality.

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AAAI Conference 2024 Conference Paper

How to Trade Off the Quantity and Capacity of Teacher Ensemble: Learning Categorical Distribution to Stochastically Employ a Teacher for Distillation

Zixiang Ding
Guoqing Jiang
Shuai Zhang
Lin Guo
Wei Lin

We observe two phenomenons with respect to quantity and capacity: 1) more teacher is not always better for multi-teacher knowledge distillation, and 2) stronger teacher is not always better for single-teacher knowledge distillation. To trade off the quantity and capacity of teacher ensemble, in this paper, we propose a new distillation paradigm named Dynamic Knowledge Distillation (DynaKD) that learn an adaptive categorical distribution to stochastically employ a teacher from a teacher ensemble in each step, to transfer knowledge from teacher ensemble into student. DynaKD has three advantages: 1) it can preserve diversity of each teacher via one-to-one distillation manner instead of several-for-one, 2) it can make the best of powerful teacher via those multi-level assistant teachers in ensemble, and 3) it can also dynamically determine the importance of each teacher for various tasks. To verify the effectiveness of the proposed approach, we conduct extensive experiments for BERT compression on GLUE benchmark. Experimental results show that the proposed approach achieves state-of-the-art score compared to previous compression approaches on five out of seven downstream tasks, including pushing MRPC F1 and accuracy to 92.2 (1.4 point absolute improvement), RTE accuracy to 76.2 (2.8 point absolute improvement). Moreover, we conduct also extensive experiments for image classification on CIFAR-100. Similarly, DynaKD achieves also state-of-the-art performance.

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AAAI Conference 2023 Conference Paper

SKDBERT: Compressing BERT via Stochastic Knowledge Distillation

Zixiang Ding
Guoqing Jiang
Shuai Zhang
Lin Guo
Wei Lin

In this paper, we propose Stochastic Knowledge Distillation (SKD) to obtain compact BERT-style language model dubbed SKDBERT. In each distillation iteration, SKD samples a teacher model from a pre-defined teacher team, which consists of multiple teacher models with multi-level capacities, to transfer knowledge into student model in an one-to-one manner. Sampling distribution plays an important role in SKD. We heuristically present three types of sampling distributions to assign appropriate probabilities for multi-level teacher models. SKD has two advantages: 1) it can preserve the diversities of multi-level teacher models via stochastically sampling single teacher model in each distillation iteration, and 2) it can also improve the efficacy of knowledge distillation via multi-level teacher models when large capacity gap exists between the teacher model and the student model. Experimental results on GLUE benchmark show that SKDBERT reduces the size of a BERT model by 40% while retaining 99.5% performances of language understanding and being 100% faster.

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NeurIPS Conference 2022 Conference Paper

One-Inlier is First: Towards Efficient Position Encoding for Point Cloud Registration

Fan Yang
Lin Guo
Zhi Chen
Wenbing Tao

Transformer architecture has shown great potential for many visual tasks, including point cloud registration. As an order-aware module, position encoding plays an important role in Transformer architecture applied to point cloud registration task. In this paper, we propose OIF-PCR, a one-inlier based position encoding method for point cloud registration network. Specifically, we first find one correspondence by a differentiable optimal transport layer, and use it to normalize each point for position encoding. It can eliminate the challenges brought by the different reference frames of two point clouds, and mitigate the feature ambiguity by learning the spatial consistency. Then, we propose a joint approach for establishing correspondence and position encoding, presenting an iterative optimization process. Finally, we design a progressive way for point cloud alignment and feature learning to gradually optimize the rigid transformation. The proposed position encoding is very efficient, requiring only a small addition of memory and computing overhead. Extensive experiments demonstrate the proposed method can achieve competitive performance with the state-of-the-art methods in both indoor and outdoor scenes.

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ICRA Conference 1994 Conference Paper

A Climbing Robot with Continuous Motion

Lin Guo
Kevin J. Rogers
Robin Kirkham

A walking robot capable of moving with continuous motion on horizontal, vertical and over-hung curved surfaces has been developed. The robot is capable of moving a tool at a specified speed to follow complex paths on steel surfaces. The design, operation and control of the robot is discussed and some test results are presented. >

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