Author name cluster

Yi Shi

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

11 papers

1 author row

EAAI Journal 2026 Journal Article

Cross-stain knowledge distillation for low-cost lung cancer programmed death ligand-1 assessment with multi-granularity multiple instance learning

Yi Shi
Chong Ge
Fang Zhao
Anli Zhang
Ao Li
Haibo Wu
Minghui Wang

Accurately assessing programmed death ligand-1 (PD-L1) status, recognizing patients potentially responsive to immunotherapies. Since immunohistochemistry (IHC) staining is gold standard in identifying molecular biomarker but often expensive and unattainable, routine hematoxylin and eosin (H&E) staining offers a low-cost alternative. However, H&E images primarily reveal morphological knowledge and inherently lack PD-L1-related molecular information, resulting in a severe mono-stain knowledge limitation. Additionally, most existing approaches analyze gigapixel whole-slide images at only a single magnification, which fails to unravel complex pathological information across granularities, leading to a significant uni-granularity information limitation. Therefore, we propose an innovative cross-stain knowledge distillation with multi-granularity framework, namely CroSMuG. First, to alleviate uni-granularity information limitation, a new multi-granularity multiple instance learning framework is introduced. This is based on macro-micro dual branches, which comprises a macro-branch and a micro-branch to extract global and local pathological information. Furthermore, we develop a novel cross-stain knowledge distillation strategy featuring triple-united distillation loss. Specifically, this approach introduces globality-, locality- and task-aware knowledge distillation, enabling the H&E-based predictive network as a student to learn crucial molecular knowledge from an IHC teacher network. Extensive experiments are conducted on diverse real-world datasets from multiple medical centers, and CroSMuG achieves the superior performance with area under the curve (AUC) of 83. 6 % on internal dataset and 81. 2 % on external dataset. These results highlight the generalizability of CroSMuG for accurate H&E-based PD-L1 assessment, offering the potential for practical applications in lung cancer immunotherapy decision-making in clinical practices.

Details DOI

AAAI Conference 2026 Conference Paper

Enhancing Meme Emotion Understanding with Multi-Level Modality Enhancement and Dual-Stage Modal Fusion

Yi Shi
Wenlong Meng
Zhenyuan Guo
Chengkun Wei
Wenzhi Chen

With the rapid rise of social media and Internet culture, memes have become a popular medium for expressing emotional tendencies. This has sparked growing interest in Meme Emotion Understanding (MEU), which aims to classify the emotional intent behind memes by leveraging their multimodal contents. While existing efforts have achieved promising results, two major challenges remain: (1) a lack of fine-grained multimodal fusion strategies, and (2) insufficient mining of memes' implicit meanings and background knowledge. To address these challenges, we propose MemoDetector, a novel framework for advancing MEU. First, we introduce a four-step textual enhancement module that utilizes the rich knowledge and reasoning capabilities of Multimodal Large Language Models (MLLMs) to progressively infer and extract implicit and contextual insights from memes. These enhanced texts significantly enrich the original meme contents and provide valuable guidance for downstream classification. Next, we design a dual-stage modal fusion strategy: the first stage performs shallow fusion on raw meme image and text, while the second stage deeply integrates the enhanced visual and textual features. This hierarchical fusion enables the model to better capture nuanced cross-modal emotional cues. Experiments on two datasets, MET-MEME and MOOD, demonstrate that our method consistently outperforms state-of-the-art baselines. Specifically, MemoDetector improves F1 scores by 4.3% on MET-MEME and 3.4% on MOOD. Further ablation studies and in-depth analyses validate the effectiveness and robustness of our approach, highlighting its strong potential for advancing MEU.

PDF Details DOI

NeurIPS Conference 2025 Conference Paper

Adaptive Context Length Optimization with Low-Frequency Truncation for Multi-Agent Reinforcement Learning

Wenchang Duan
Yaoliang Yu
Jiwan He
Yi Shi

Recently, deep multi-agent reinforcement learning (MARL) has demonstrated promising performance for solving challenging tasks, such as long-term dependencies and non-Markovian environments. Its success is partly attributed to conditioning policies on large fixed context length. However, such large fixed context lengths may lead to limited exploration efficiency and redundant information. In this paper, we propose a novel MARL framework to obtain adaptive and effective contextual information. Specifically, we design a central agent that dynamically optimizes context length via temporal gradient analysis, enhancing exploration to facilitate convergence to global optima in MARL. Furthermore, to enhance the adaptive optimization capability of the context length, we present an efficient input representation for the central agent, which effectively filters redundant information. By leveraging a Fourier-based low-frequency truncation method, we extract global temporal trends across decentralized agents, providing an effective and efficient representation of the MARL environment. Extensive experiments demonstrate that the proposed method achieves state-of-the-art (SOTA) performance on long-term dependency tasks, including PettingZoo, MiniGrid, Google Research Football (GRF), and StarCraft Multi-Agent Challenge v2 (SMACv2).

PDF Details

JBHI Journal 2025 Journal Article

Agnostic-Specific Modality Learning for Cancer Survival Prediction From Multiple Data

Honglei Liu
Yi Shi
Ying Xu
Ao Li
Minghui Wang

Cancer is a pressing public health problem and one of the main causes of mortality worldwide. The development of advanced computational methods for predicting cancer survival is pivotal in aiding clinicians to formulate effective treatment strategies and improve patient quality of life. Recent advances in survival prediction methods show that integrating diverse information from various cancer-related data, such as pathological images and genomics, is crucial for improving prediction accuracy. Despite promising results of existing approaches, there are great challenges of modality gap and semantic redundancy presented in multiple cancer data, which could hinder the comprehensive integration and pose substantial obstacles to further enhancing cancer survival prediction. In this study, we propose a novel agnostic-specific modality learning (ASML) framework for accurate cancer survival prediction. To bridge the modality gap and provide a comprehensive view of distinct data modalities, we employ an agnostic-specific learning strategy to learn the commonality across modalities and the uniqueness of each modality. Moreover, a cross-modal fusion network is exerted to integrate multimodal information by modeling modality correlations and diminish semantic redundancy in a divide-and-conquer manner. Extensive experiment results on three TCGA datasets demonstrate that ASML reaches better performance than other existing cancer survival prediction methods for multiple data.

Details DOI

JBHI Journal 2025 Journal Article

DRLSurv: Disentangled Representation Learning for Cancer Survival Prediction by Mining Multimodal Consistency and Complementarity

Ying Xu
Yi Shi
Honglei Liu
Ao Li
Anli Zhang
Minghui Wang

Accurate cancer survival prediction is crucial in devising optimal treatment plans and offering individualized care to improve clinical outcomes. Recent researches confirm that integrating heterogenous cancer data such as histopathological images and genomic data, can enhance our understanding of cancer progression and provides a multimodal perspective on patient survival chances. However, existing methods often over-look the fundamental aspects of multimodal data, i. e. , consistency and complementarity, which in consequence significantly hinder advancements in cancer survival prediction. To address this issue, we represent DRLSurv, a novel multimodal deep learning method that leverages disentangled representation learning for precise cancer survival prediction. Through dedicated deep encoding networks, DRLSurv decomposes each modality into modality-invariant and modality-specific representations, which are mapped to common and unique feature subspaces for simultaneously mining the distinct aspects of cancer multimodal data. Moreover, our method innovatively introduces a subspace-based proximity contrastive loss and re-disentanglement loss, thus ensuring the successful decomposition of consistent and complementary information while maintaining the multimodal fidelity during the learning of disentangled representations. Both quantitative analyses and visual assessments on different datasets validate the superiority of DRLSurv over existing survival prediction approaches, demonstrating its powerful capability to exploit enriched survival-related information from cancer multimodal data. Therefore, DRLSurv not only offers a unified and comprehensive deep learning framework for advancing multimodal survival predictions, but also provides valuable insights for cancer prognosis and survival analysis.

Details DOI

JBHI Journal 2025 Journal Article

MIF: Multi-Shot Interactive Fusion Model for Cancer Survival Prediction Using Pathological Image and Genomic Data

Yi Shi
Minghui Wang
Honglei Liu
Fang Zhao
Ao Li
Xun Chen

Accurate cancer survival prediction is crucial for oncologists to determine therapeutic plan, which directly influences the treatment efficacy and survival outcome of patient. Recently, multimodal fusion-based prognostic methods have demonstrated effectiveness for survival prediction by fusing diverse cancer-related data from different medical modalities, e. g. , pathological images and genomic data. However, these works still face significant challenges. First, most approaches attempt multimodal fusion by simple one-shot fusion strategy, which is insufficient to explore complex interactions underlying in highly disparate multimodal data. Second, current methods for investigating multimodal interactions face the capability-efficiency dilemma, which is the difficult balance between powerful modeling capability and applicable computational efficiency, thus impeding effective multimodal fusion. In this study, to encounter these challenges, we propose an innovative multi-shot interactive fusion method named MIF for precise survival prediction by utilizing pathological and genomic data. Particularly, a novel multi-shot fusion framework is introduced to promote multimodal fusion by decomposing it into successive fusing stages, thus delicately integrating modalities in a progressive way. Moreover, to address the capacity-efficiency dilemma, various affinity-based interactive modules are introduced to synergize the multi-shot framework. Specifically, by harnessing comprehensive affinity information as guidance for mining interactions, the proposed interactive modules can efficiently generate low-dimensional discriminative multimodal representations. Extensive experiments on different cancer datasets unravel that our method not only successfully achieves state-of-the-art performance by performing effective multimodal fusion, but also possesses high computational efficiency compared to existing survival prediction methods.

Details DOI

AAAI Conference 2025 Conference Paper

SpeHeaTal: A Cluster-Enhanced Segmentation Method for Sperm Morphology Analysis

Yi Shi
Yun-Kai Wang
Xu-Peng Tian
Tie-Yi Zhang
Bing Yao
Hui Wang
Yong Shao
Cen-Cen Wang

The accurate assessment of sperm morphology is crucial in andrological diagnostics, where the segmentation of sperm images presents significant challenges. Existing approaches frequently rely on large annotated datasets and often struggle with the segmentation of overlapping sperm and the presence of dye impurities. To address these challenges, this paper first analyzes the issue of overlapping sperm tails from a geometric perspective and introduces a novel clustering algorithm, Con2Dis, which effectively segments overlapping tails by considering three essential factors: CONnectivity, CONformity, and DIStance. Building on this foundation, we propose an unsupervised method, SpeHeaTal, designed for the comprehensive segmentation of the SPErm HEAd and TAiL. SpeHeaTal employs the Segment Anything Model (SAM) to generate masks for sperm heads while filtering out dye impurities, utilizes Con2Dis to segment tails, and then applies a tailored mask splicing technique to produce complete sperm masks. Experimental results underscore the superior performance of SpeHeaTal, particularly in handling images with overlapping sperm.

PDF Details DOI

EAAI Journal 2024 Journal Article

Multi-order feature interaction-aware intrusion detection scheme for ensuring cyber security of intelligent connected vehicles

Weifeng Gong
Shichun Yang
Haoran Guang
Bin Ma
Bowen Zheng
Yi Shi
Baotian Li
Yaoguang Cao

The evolution of technology has raised concerns regarding cybersecurity for intelligent connected vehicles (ICVs). In-vehicle network in ICVs lacks robust protection mechanisms, making it vulnerable to cyber threats. In response, intrusion detection systems (IDSs) for ICVs have been developed to protect vehicles from malicious cyber attacks. However, current IDS methods solely rely on independent features, limiting their learning capabilities and increasing the number of false detections. Moreover, many IDSs require the knowledge of mapping between network messages and contents, which restricts their application. To address these limitations, we propose the Multi-order Feature Interaction-aware Intrusion Detection (MIFI) scheme for ICVs. Feature attention cross network is designed to address higher-order feature interactions, while factorization machine is used for second-order interactions. Then a discriminator is utilized to detect the attacks. MIFI expands the feature space through features interaction, thereby enhancing its ability to detect attacks. Moreover, it perceives the relationships of vehicle messages, facilitating intrusion detection without knowing the corresponding rules of vehicle messages. The performance of the proposed method is evaluated on two real-vehicle datasets, affirming its effectiveness and robustness. MIFI achieves an accuracy of over 99% in detecting different attacks. The proposed method can improve the accuracy of traditional IDS to a maximum of 99. 99%, and increase the highest F1-score to 97. 18%, demonstrating the model’s ability of achieving multi-order feature interactions. Ultimately, MIFI is suitable for intrusion detection in different types of ICV networks, significantly contributing to the cybersecurity of ICVs.

Details DOI

TCS Journal 2022 Journal Article

A simple linear time algorithm to solve the MIST problem on interval graphs

Peng Li
Jianhui Shang
Yi Shi

Motivated by the design of cost-efficient communication networks, the problem about Maximum Internal Spanning Tree that arises in a connected graph, is proposed to find a spanning tree with the maximum number of internal vertices. In 2018, Xingfu Li et al. presented a polynomial algorithm to find a maximum internal spanning tree in a connected interval graph. Based on the structure of normal orderings on interval graphs, we present a simple linear time algorithm that solve the problem when restricted to connected interval graphs in this paper. The proof provides additional insight about the linear time algorithm on interval graphs.

Details DOI

JBHI Journal 2022 Journal Article

Multi-Dimensional Feature Combination Method for Continuous Blood Pressure Measurement Based on Wrist PPG Sensor

Pan Yao
Ning Xue
Siyuan Yin
Changhua You
Yusen Guo
Yi Shi
Tiezhu Liu
Lei Yao

The cuff-less blood pressure (BP) monitoring method based on photoplethysmo- gram (PPG) makes it possible for long-term BP monitoring to prevent and treat cardiovascular and cerebrovascular events. In this paper, a portable BP prediction system based on feature combination and artificial neural network (ANN) is implemented. The robustness of the model is improved from three aspects. Firstly, an adaptive peak extraction algorithm was used to improve the accuracy of peaks and troughs detection. Secondly, multi-dimensional features were extracted and fused, including three groups of PPG-based features and one group of demographics-based features. Finally, a two-layer feedforward artificial neural networks algorithm was used for regression. Thirty-three subjects distributed in the three BP groups were recruited. The proposed method passed the European Society of Hypertension International Protocol revision 2010 (ESP-IP2). Experimental results show that the proposed method exhibits good accuracy for a diverse population with an estimation error of −0. 07 ± 4. 47 mmHg for SBP and 0. 00 ± 3. 61 mmHg for DBP. Moreover, the model tracked the BP of two subjects for half a month, laying the foundation work for daily BP monitoring. This work will contribute to the long-term wellness management and rehabilitation process, enabling timely detection and improvement of the user's physical health.

Details DOI

TCS Journal 2021 Journal Article

The longest cycle problem is polynomial on interval graphs

Jianhui Shang
Peng Li
Yi Shi

The longest cycle problem is the problem of finding a cycle with maximal vertices in a graph. Although it is solvable in polynomial time on few trivial graph classes, the longest cycle problem is well known as NP-complete. A lot of efforts have been devoted to the longest cycle problem. To the best of our knowledge however, there are no polynomial algorithms that can solve any of the non-trivial graph classes. Interval graphs, the intersection of chordal graphs and asteroidal triple-free graphs, are known to be the non-trial graph classes that have polynomial algorithm of the longest cycle problem. In 2009, K. Ioannidou, G. B. Mertzios and S. D. Nikolopoulos presented a polynomial algorithm for the longest path problem on interval graphs in Ioannidou et al. (2009) [19]. Inspired by their work, we investigate the longest cycle problem of interval graphs. In this paper, we present the first polynomial algorithm for the longest cycle problem on interval graphs. A dynamic programming approach is proposed in the polynomial algorithm that runs in O ( n 8 ) time, where n is the number of vertices of the input graph. Using a similar approach, we design a polynomial algorithm to solve the longest k-thick subgraph problem on interval graphs which will be presented in another separate work. According to the interesting properties of k-thick interval graphs that we discovered (e. g. , an interval graph G is traceable if and only if G is 1-thick, G is hamiltonian if and only if G is 2-thick, G is hamiltonian connected if and only if G is 3-thick and so on), the algorithm presented in this paper can be important in studying the spanning connectivity on interval graphs.

Details DOI