Shaoxin Li

JBHI Journal 2025 Journal Article

Robust Federated Video-based Remote Physiological Measurement for Heterogeneous Multi-source Data

• Wenan Wang
• Qinwei Xu
• Xinkun Xu
• Shaoxin Li
• Feiyue Huang
• Yefeng Zheng
• Lifeng Zhu
• Zhian Bai

Remote photoplethysmography (rPPG) is a technology that uses facial video to capture physiological signals, namely photoplethysmography (PPG) signals, enabling convenient and low-cost non-contact physiolog ical measurement. Although current rPPG methods have achieved remarkable performance through extensive data training, they often ignore the transmission cost and privacy concerns of rPPG data. Federated learning (FL) pro vides a promising solution to these issues. However, effective federated training remains challenging due to the cross-domain heterogeneity that arises when using multi source rPPG data. To address these challenges, we characterize the heterogeneity of multi-source rPPG data from both the input and output domain perspectives. Motivated by this analysis, we introduce pseudo-labeling techniques and propose a novel FL framework, FedGRC. Specifically, we incorporate automatic gradient regularization calibration to mitigate the impact of input domain discrepancies on model performance. Furthermore, we leverage pseudo labels generated by handcrafted rPPG methods to align the output domain and reduce label inconsistencies across datasets. Our approach is validated across six publicly available datasets, demonstrating significant advantages over other approaches and effectively addressing the challenges of privacy protection and heterogeneity of multisource data.

JBHI Journal 2025 Journal Article

Verification is All You Need: Prompting Large Language Models for Zero-Shot Clinical Coding

• Shaoxin Li
• Can Zheng
• Jiaxiang Wu
• Qinwei Xu
• Xingkun Xu
• Hanyang Wang
• Yingkai Sun
• Zhian Bai

Clinical coding translates medical information from Electronic Health Records (EHRs) into structured codes such as ICD-10, which are essential for healthcare applications. Advances in deep learning and natural language processing have enabled automatic ICD coding models to achieve notable accuracy metrics on in-domain datasets when adequately trained. However, the scarcity of clinical medical texts and the variability across different datasets pose significant challenges, making it difficult for current state-of-the-art models to ensure robust generalization performance across diverse data distributions. Recent advances in Large Language Models (LLMs), such as GPT-4o, have shown great generalization capabilities across general domains and potential in medical information processing tasks. However, their performance in generating clinical codes remains suboptimal. In this study, we propose a novel ICD coding paradigm based on code verification to leverage the capabilities of LLMs. Instead of directly generating accurate codes from a vast code space, we simplify the task by verifying the code assignment from a given candidate set. Through extensive experiments, we demonstrate that LLMs function more effectively as code verifiers rather than code generators, with GPT-4o achieving the best performance on the CodiEsp dataset under zero-shot settings. Furthermore, our results indicate that LLM-based systems can perform on par with state-of-the-art clinical coding systems while offering superior generalizability across institutions, languages, and ICD versions.

AAAI Conference 2021 Conference Paper

Graph Game Embedding

• Xiaobin Hong
• Tong Zhang
• Zhen Cui
• Yuge Huang
• Pengcheng Shen
• Shaoxin Li
• Jian Yang

Graph embedding aims to encode nodes/edges into lowdimensional continuous features, and has become a crucial tool for graph analysis including graph/node classification, link prediction, etc. In this paper we propose a novel graph learning framework, named graph game embedding, to learn discriminative node representation as well as encode graph structures. Inspired by the spirit of game learning, node embedding is converted to the selection/searching process of player strategies, where each node corresponds to one player and each edge corresponds to the interaction of two players. Then, a utility function, which theoretically satisfies the Nash Equilibrium, is defined to measure the benefit/loss of players during graph evolution. Furthermore, a collaboration and competition mechanism is introduced to increase the discriminant learning ability. Under this graph game embedding framework, considering different interaction manners of nodes, we propose two specific models, named paired game embedding for paired nodes and group game embedding for group interaction. Comparing with existing graph embedding methods, our algorithm possesses two advantages: (1) the designed utility function ensures the stable graph evolution with theoretical convergence and Nash Equilibrium satisfaction; (2) the introduced collaboration and competition mechanism endows the graph game embedding framework with discriminative feature leaning ability by guiding each node to learn an optimal strategy distinguished from others. We test the proposed method on three public datasets about citation networks, and the experimental results verify the effectiveness of our method.

EAAI Journal 2019 Journal Article

A novel algorithm for privacy preserving utility mining based on integer linear programming

• Shaoxin Li
• Nankun Mu
• Junqing Le
• Xiaofeng Liao