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Man-Sheng Chen

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JBHI Journal 2025 Journal Article

Interpretable Staging Prediction of Liver Cancer Based on Joint-Knowledge Network

Xuecong Zheng
Ya Li
Zhiqi Wu
Yiyang Tang
Pei-Yuan Lai
Man-Sheng Chen
Hong-Yi Chen
Chang-Dong Wang

Clinical staging is crucial for treatment strategies and improving 5-year survival rates in hepatocellular carcinoma (HCC) patients. However, existing methods struggle to distinguish stages with highly similar textual features. Additionally, their lack of interpretability hampers their practical application in medical scenarios. Here, we introduce KnowST, a joint-knowledge network designed to leverage task relevance to explore implicit knowledge for interpretable staging prediction of liver cancer. First, the relevance of auxiliary tasks and the main task is established from two perspectives to guide the model's focus on staging-related implicit knowledge in radiology reports. Stages-to-stages: KnowST learns the inter-stage distinctions between different stages and the similarities within the same stages, using these as important references for staging differentiation. Factors-to-stages: Clinically, staging is determined by multiple tumor factors. These factors can serve as effective clues to assist KnowST in predicting the correct stage, especially in the case of confusing stages. Second, domain-specific word embeddings are introduced to bridge the gap between pre-trained language models and Chinese radiology reports. Lastly, tumor factor prediction enhances the credibility of the deep model in staging prediction, and its visualized results effectively demonstrate the model's interpretability. Overall, KnowST leverages the joint-knowledge from these two perspectives, effectively utilizing implicit information in radiology reports to achieve interpretable clinical staging. Compared to the optimal baselines, KnowST improves AUC by 7. 69% and achieves 90. 52% accuracy on 573 real-world radiology reports, while also demonstrating superior stage identification and stable performance across various metrics.

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JBHI Journal 2025 Journal Article

“Pre-Train, Prompt” Framework to Boost Graph Neural Networks Performance in EEG Analysis

Can-Ming Cui
Hong-Yi Chen
Man-Sheng Chen
Jiahong Li
Zhaopeng Tong
Chanmei Fang
Chang-Dong Wang
Yuexin Cai

Electroencephalography (EEG) is a vital non-invasive technique used in neuroscience research and clinical diagnosis. However, EEG data have a complex non-Euclidean structure and are often scarce, making training effective graph neural network (GNN) models difficult. We propose a “pre-train, prompt” framework in graph neural networks for EEG analysis, called GNN-based EEG Prompt Learning (GEPL). The framework first uses unsupervised contrastive learning to pre-train on a large-scale EEG dataset. It then transfers the generic EEG knowledge learned by the model to target EEG datasets through graph prompt learning, thereby enhancing the model's performance with a limited amount of EEG data from the target domain. We tested the framework on five EEG datasets, and the results showed that GEPL outperformed traditional fine-tuning methods in classification accuracy and area under the ROC curve (AUC). GEPL demonstrated improved generalization, robustness, and computational efficiency, thereby significantly reducing the overfitting risks associated with limited EEG data. Moreover, the model provided interpretable results, highlighting relevant brain regions during classification tasks. This research suggests that the “pre-train, prompt” paradigm is well-suited for EEG analysis and offers potential applications in other domains where data are limited.

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AAAI Conference 2020 Conference Paper

Multi-View Clustering in Latent Embedding Space

Man-Sheng Chen
Ling Huang
Chang-Dong Wang
Dong Huang

Previous multi-view clustering algorithms mostly partition the multi-view data in their original feature space, the efﬁcacy of which heavily and implicitly relies on the quality of the original feature presentation. In light of this, this paper proposes a novel approach termed Multi-view Clustering in Latent Embedding Space (MCLES), which is able to cluster the multi-view data in a learned latent embedding space while simultaneously learning the global structure and the cluster indicator matrix in a uniﬁed optimization framework. Specifically, in our framework, a latent embedding representation is ﬁrstly discovered which can effectively exploit the complementary information from different views. The global structure learning is then performed based on the learned latent embedding representation. Further, the cluster indicator matrix can be acquired directly with the learned global structure. An alternating optimization scheme is introduced to solve the optimization problem. Extensive experiments conducted on several real-world multi-view datasets have demonstrated the superiority of our approach.

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