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Chen Ye

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8 papers

1 author row

TIST Journal 2026 Journal Article

From Hallucination to Certainty: Meta-Knowledge Guided Self-Correcting Large Language Models

Wei Zhang
Guojun Dai
Ding Luo
Yan Wang
Chen Ye

Recent advancements in Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation. To further enhance their factual grounding and reasoning fidelity, integrating LLMs with Knowledge Graphs (KGs) has emerged as a promising direction. Significant progress has been made in leveraging KGs to augment LLM reasoning through methods like Retrieval-Augmented Generation. However, effectively harnessing the synergy between LLMs and KGs for robust and reliable reasoning still presents critical challenges. Specifically: (1) LLMs struggle to effectively interpret and utilize the structured nature of KGs, due to the discrepancy between their text-based training and KG's symbolic representations; (2) querying and reasoning over structured knowledge in KGs remains inefficient for LLMs, hindering complex inference. To address these limitations, we introduce Meta-Knowledge enhanced Knowledge Graph (MKG), a novel framework that empowers LLMs to effectively leverage structured knowledge from KGs. MKG employs Meta-Knowledge, stored in a multi-store memory with a Self-Correcting Mechanism, to guide LLMs in KG retrieval and reasoning. Our experimental evaluations on complex question answering benchmarks demonstrate that MKG achieves significant performance gains, outperforms the baseline Original LLM, Retrieval-Augmented Generation (RAG), ReAct, GraphRAG and ToG frameworks by 25%, 17%, 11%, 3.3% and 2.6%, respectively.

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AAAI Conference 2026 Conference Paper

Know Your Neighbors: Subgraph Importance Sampling for Heterophilic Graph Active Learning

Wenjie Yang
Shengzhong Zhang
Chen Ye
Jiaxing Guo
Tongshan Xu
zengfeng Huang

Graph neural networks (GNNs) have demonstrated strong performance in various graph mining tasks but rely heavily on extensively labeled nodes. To improve training efficiency, graph active learning (GAL) has emerged as a solution for selecting the most informative nodes for labeling. However, existing GAL methods are primarily designed for homophilic graphs, where nodes with the same labels are more likely to be connected. In this work, we systematically study active learning on heterophilic graphs, a setting that has received limited attention. Surprisingly, we observe that existing GAL methods fail to consistently outperform random sampling on heterophilic graphs. Through an in-depth investigation, we reveal that these methods implicitly assume homophily even on heterophilic graphs, leading to suboptimal performance. To address this issue, we introduce the principle of "Know Your Neighbors" and propose an active learning algorithm KyN specifically for heterophilic graphs. The core idea of KyN is to provide GNNs with accurate estimations of homophily distribution by labeling nodes together with their neighbors. We implement KyN based on subgraph sampling with probabilities proportional to l1 Lewis weights, which is supported by solid theoretical guarantees. Extensive experiments on diverse real-world datasets, including a large heterophilic graph with over 2 million nodes, demonstrate the effectiveness and scalability of KyN.

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JBHI Journal 2025 Journal Article

Multi-Scale Dynamic Sparse Attention UNet for Medical Image Segmentation

Xiang Li
Chong Fu
Qun Wang
Wenchao Zhang
Chen Ye
Junxin Chen
Chiu-Wing Sham

Transformers have recently gained significant attention in medical image segmentation due to their ability to capture long-range dependencies. However, the presence of excessive background noise in large regions of medical images introduces distractions and increases the computational burden on the fine-grained self-attention (SA) mechanism, which is a key component of the transformer model. Meanwhile, preserving fine-grained details is essential for accurately segmenting complex, blurred medical images with diverse shapes and sizes. Thus, we propose a novel Multi-scale Dynamic Sparse Attention (MDSA) module, which flexibly reduces computational costs while maintaining multi-scale fine-grained interactions with content awareness. Specifically, multi-scale aggregation is first applied to the feature maps to enrich the diversity of interaction information. Then, for each query, irrelevant key-value pairs are filtered out at a coarse-grained level. Finally, fine-grained SA is performed on the remaining key-value pairs. In addition, we design an enhanced downsampling merging (EDM) module and an enhanced upsampling fusion (EUF) module for building pyramid architectures. Using MDSA to construct the basic blocks, combined with EDMs and EUFs, we develop a UNet-like model named MDSA-UNet. Since MDSA-UNet dynamically processes only a small subset of relevant fine-grained features, it achieves strong segmentation performance with high computational efficiency. Extensive experiments on four datasets spanning three different types demonstrate that our MDSA-UNet, without using pre-training, significantly outperforms other non-pretrained methods and even competes with pre-trained models, achieving Dice scores of 82. 10% on DDTI, 80. 20% on TN3K, 90. 75% on ISIC2018, and 91. 05% on ACDC. Meanwhile, our model maintains lower complexity, with only 6. 65 M parameters and 4. 54 G FLOPs at a resolution of 224 × 224, ensuring both effectiveness and efficiency. Code is available at URL.

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NeurIPS Conference 2025 Conference Paper

PanTS: The Pancreatic Tumor Segmentation Dataset

Wenxuan Li
Xinze Zhou
Qi Chen
Tianyu Lin
Pedro R. A. S. Bassi
Xiaoxi Chen
Chen Ye
Zheren Zhu

PanTS is a large-scale, multi-institutional dataset curated to advance research in pancreatic CT analysis. It contains 36, 390 CT scans from 145 medical centers, with expert-validated, voxel-wise annotations of over 993, 000 anatomical structures, covering pancreatic tumors, pancreas head, body, and tail, and 24 surrounding anatomical structures such as vascular/skeletal structures and abdominal/thoracic organs. Each scan includes metadata such as patient age, sex, diagnosis, contrast phase, in-plane spacing, slice thickness, etc. AI models trained on PanTS achieve significantly better performance in pancreatic tumor detection, localization, and segmentation than those trained on existing public datasets. Our analysis indicates that these gains are directly attributable to the 16× larger-scale tumor annotations and indirectly supported by the 24 additional surrounding anatomical structures. As the largest and most comprehensive resource of its kind, PanTS offers a new benchmark for developing and evaluating AI models in pancreatic CT analysis.

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NeurIPS Conference 2024 Conference Paper

Focus On What Matters: Separated Models For Visual-Based RL Generalization

Di Zhang
Bowen Lv
Hai Zhang
Feifan Yang
Junqiao Zhao
Hang Yu
Chang Huang
Hongtu Zhou

A primary challenge for visual-based Reinforcement Learning (RL) is to generalize effectively across unseen environments. Although previous studies have explored different auxiliary tasks to enhance generalization, few adopt image reconstruction due to concerns about exacerbating overfitting to task-irrelevant features during training. Perceiving the pre-eminence of image reconstruction in representation learning, we propose SMG (\blue{S}eparated \blue{M}odels for \blue{G}eneralization), a novel approach that exploits image reconstruction for generalization. SMG introduces two model branches to extract task-relevant and task-irrelevant representations separately from visual observations via cooperatively reconstruction. Built upon this architecture, we further emphasize the importance of task-relevant features for generalization. Specifically, SMG incorporates two additional consistency losses to guide the agent's focus toward task-relevant areas across different scenarios, thereby achieving free from overfitting. Extensive experiments in DMC demonstrate the SOTA performance of SMG in generalization, particularly excelling in video-background settings. Evaluations on robotic manipulation tasks further confirm the robustness of SMG in real-world applications. Source code is available at \url{https: //anonymous. 4open. science/r/SMG/}.

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JBHI Journal 2024 Journal Article

Progressive Dual Priori Network for Generalized Breast Tumor Segmentation

Li Wang
Lihui Wang
Zixiang Kuai
Lei Tang
Yingfeng Ou
Min Wu
Tianliang Shi
Chen Ye

To promote the generalization ability of breast tumor segmentation models, as well as to improve the segmentation performance for breast tumors with smaller size, low-contrast and irregular shape, we propose a progressive dual priori network (PDPNet) to segment breast tumors from dynamic enhanced magnetic resonance images (DCE-MRI) acquired at different centers. The PDPNet first cropped tumor regions with a coarse-segmentation based localization module, then the breast tumor mask was progressively refined by using the weak semantic priori and cross-scale correlation prior knowledge. To validate the effectiveness of PDPNet, we compared it with several state-of-the-art methods on multi-center datasets. The results showed that, comparing against the suboptimal method, the DSC and HD95 of PDPNet were improved at least by 5. 13% and 7. 58% respectively on multi-center test sets. In addition, through ablations, we demonstrated that the proposed localization module can decrease the influence of normal tissues and therefore improve the generalization ability of the model. The weak semantic priors allow focusing on tumor regions to avoid missing small tumors and low-contrast tumors. The cross-scale correlation priors are beneficial for promoting the shape-aware ability for irregular tumors. Thus integrating them in a unified framework improved the multi-center breast tumor segmentation performance.

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AAAI Conference 2024 Conference Paper

Towards Automated Chinese Ancient Character Restoration: A Diffusion-Based Method with a New Dataset

Haolong Li
Chenghao Du
Ziheng Jiang
Yifan Zhang
Jiawei Ma
Chen Ye

Automated Chinese ancient character restoration (ACACR) remains a challenging task due to its historical significance and aesthetic complexity. Existing methods are constrained by non-professional masks and even overfitting when training on small-scale datasets, which hinder their interdisciplinary application to traditional fields. In this paper, we are proud to introduce the Chinese Ancient Rubbing and Manuscript Character Dataset (ARMCD), which consists of 15,553 real-world ancient single-character images with 42 rubbings and manuscripts, covering the works of over 200 calligraphy artists spanning from 200 to 1,800 AD. We are also dedicated to providing professional synthetic masks by extracting localized erosion from real eroded images. Moreover, we propose DiffACR (Diffusion model for automated Chinese Ancient Character Restoration), a diffusion-based method for the ACACR task. Specifically, we regard the synthesis of eroded images as a special form of cold diffusion on uneroded ones and extract the prior mask directly from the eroded images. Our experiments demonstrate that our method comprehensively outperforms most existing methods on the proposed ARMCD. Dataset and code are available at https://github.com/lhl322001/DiffACR.

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NeurIPS Conference 2023 Conference Paper

How to Fine-tune the Model: Unified Model Shift and Model Bias Policy Optimization

Hai Zhang
Hang Yu
Junqiao Zhao
Di Zhang
Xiao Zhang
Hongtu Zhou
Chang Huang
Chen Ye

Designing and deriving effective model-based reinforcement learning (MBRL) algorithms with a performance improvement guarantee is challenging, mainly attributed to the high coupling between model learning and policy optimization. Many prior methods that rely on return discrepancy to guide model learning ignore the impacts of model shift, which can lead to performance deterioration due to excessive model updates. Other methods use performance difference bound to explicitly consider model shift. However, these methods rely on a fixed threshold to constrain model shift, resulting in a heavy dependence on the threshold and a lack of adaptability during the training process. In this paper, we theoretically derive an optimization objective that can unify model shift and model bias and then formulate a fine-tuning process. This process adaptively adjusts the model updates to get a performance improvement guarantee while avoiding model overfitting. Based on these, we develop a straightforward algorithm USB-PO (Unified model Shift and model Bias Policy Optimization). Empirical results show that USB-PO achieves state-of-the-art performance on several challenging benchmark tasks.

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