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Yuting Wu

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5 papers

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AAAI Conference 2026 Conference Paper

Energy-guided Dual Domain-invariant Prompting Framework with Fourier Regularization for Generalized Few-Shot Medical Segmentation

Shaolei Liu
Yuting Wu
Dongchen Zhu
Jiamao Li

Precise segmentation of organ and tissue lesions is essential for clinical diagnosis and treatment. Despite the progress of deep learning and foundation segmentation models, their domain generalization capability remains limited particularly when dealing with cross-domain scenarios or unseen data, leading to significant performance degradation. Current medical SAM-based generalization methods face two primary challenges: First, existing prompt-tuning strategies inadequately capture key domain-invariant features; Second, the reliance on fully labeled source domain data is unrealistic in clinical practice. To address these challenges, we propose a novel Dual domain-Invariant Prompt Optimization (DIPO) enhanced by energy-guided augmentation and frequency consistency regularization for few-shot medical image segmentation generalization. Our approach introduces a multi-band momentum enhancement strategy to dynamically augment source data by leveraging diverse frequency bands of the Fourier amplitude spectrum. Furthermore, we integrate multiscale geometric representation-based non-subsampled shearlet transform and text prompts to strengthen the extraction of shape- and texture-related domain-invariant features. Finally, we employ frequency consistency regularization to refine model robustness using predictions from unlabeled data. Experimental results in prostate and fundus datasets demonstrate that our method significantly outperforms current state-of-the-art methods.

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AAAI Conference 2025 Conference Paper

CognTKE: A Cognitive Temporal Knowledge Extrapolation Framework

Wei Chen
Yuting Wu
Shuhan Wu
Zhiyu Zhang
Mengqi Liao
Youfang Lin
Huaiyu Wan

Reasoning future unknowable facts on temporal knowledge graphs (TKGs) is a challenging task, holding significant academic and practical values for various fields. Existing studies exploring explainable reasoning concentrate on modeling comprehensible temporal paths relevant to the query. Yet, these path-based methods primarily focus on local temporal paths appearing in recent times, failing to capture the complex temporal paths in TKG and resulting in the loss of longer historical relations related to the query. Motivated by the Dual Process Theory in cognitive science, we propose a Cognitive Temporal Knowledge Extrapolation framework (CognTKE), which introduces a novel temporal cognitive relation directed graph (TCR-Digraph) and performs interpretable global shallow reasoning and local deep reasoning over the TCR-Digraph. Specifically, the proposed TCR-Digraph is constituted by retrieving significant local and global historical temporal relation paths associated with the query. In addition, CognTKE presents the global shallow reasoner and the local deep reasoner to perform global one-hop temporal relation reasoning (System 1) and local complex multi-hop path reasoning (System 2) over the TCR-Digraph, respectively. The experimental results on four benchmark datasets demonstrate that CognTKE achieves significant improvement in accuracy compared to the state-of-the-art baselines and delivers excellent zero-shot reasoning ability.

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EAAI Journal 2025 Journal Article

Possibilistic c-means clustering approach based on a novel weighted-kernel distance for imbalanced images with minority targets in sparsely distribution

Haiyan Yu
Yuting Wu
Haocong Zheng
Qianqian Luo
Lu Zhang

Possibilistic c-means clustering (PCM), a classic partition clustering algorithm, is an important data mining technique for unsupervised image segmentation in the field of artificial intelligence. The typicalities (memberships) of PCM have better description of local information and excellent noise resistance due to its absolute attribute. However, it still faces the challenges of segmenting color images with multiple characteristics, such as feature imbalance, cluster-size imbalance, noise attack, especially sparsely distribution of minority targets in the feature space. Therefore, this paper introduces a weighted-kernel distance-based PCM (WK-PCM) algorithm. Firstly, a weighted-kernel distance (WK-distance) is defined by combining the absolute attribute of typicalities and the Gaussian kernel function to enhance the intra-class compactness of sparse targets. Meanwhile, the feature weighting scheme in the WK-distance is expected to avoid center offset caused by imbalanced features. Secondly, to overcome the issue of center overlapping caused by insufficient interclass relationships of typicalities, the cutset theory is introduced based on the WK-distance to select partial objects. Then the typicalities of these selected objects are suppressed to increase inter-class separateness and avoid the issue of coincident clustering (also called center overlapping). Finally, an improved WK-PCM image segmentation algorithm (LWK-PCM) based on local spatial information acquired through bilateral filtering is proposed for imbalanced color images with noise corruption. Experiments conducted on synthetic datasets and imbalanced color images indicate that the proposed WK-PCM and LWK-PCM algorithms get excellent clustering performance compared to the relevant clustering algorithms.

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EAAI Journal 2024 Journal Article

Evolutionary computation and reinforcement learning integrated algorithm for distributed heterogeneous flowshop scheduling

Rui Li
Ling Wang
Wenyin Gong
Jingfang Chen
Zixiao Pan
Yuting Wu
Yang Yu

With the advancement of the global economy, there is a growing focus on distributed manufacturing. This study addresses the complex challenges posed by the distributed heterogeneous flow shop scheduling problem (DHFSP), wherein multiple machine processing speeds are taken into account. The primary objectives involve the simultaneous minimization of both makespan and total energy consumption. To tackle this intricate problem, we propose an evolutionary computation and reinforcement learning integrated algorithm (ECRLIA) approach. Initially, an optimization framework is meticulously crafted to synergistically integrate both evolutionary computation and reinforcement learning solvers. Subsequently, a multi-rule cooperation initialization is devised to expedite the pre-search process across all solvers. Following this, a competition-based cooperative evolutionary algorithm is introduced to conduct a global search, thereby providing an initial solution to the DHFSP. The interplay of competition and cooperation among individuals enhances convergence. Further, a Q-learning approach employing dual agents is designed to perform a local search, supplementing solutions that evolutionary algorithms may struggle to uncover. This learning method incorporates an auxiliary agent to evaluate the action predictions of the primary agent, ensuring more stable learning. The effectiveness of the proposed algorithm is assessed through numerical experiments, which validate the efficacy of the cooperation framework, initialization cooperation, and the enhanced Q-learning method. Furthermore, ECRLIA is benchmarked against five state-of-the-art algorithms for DHFSP, and the results affirm the significant superiority of the proposed ECRLIA in addressing DHFSP compared to other algorithms.

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IJCAI Conference 2019 Conference Paper

Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs

Yuting Wu
Xiao Liu
Yansong Feng
Zheng Wang
Rui Yan
Dongyan Zhao

Entity alignment is the task of linking entities with the same real-world identity from different knowledge graphs (KGs), which has been recently dominated by embedding-based methods. Such approaches work by learning KG representations so that entity alignment can be performed by measuring the similarities between entity embeddings. While promising, prior works in the field often fail to properly capture complex relation information that commonly exists in multi-relational KGs, leaving much room for improvement. In this paper, we propose a novel Relation-aware Dual-Graph Convolutional Network (RDGCN) to incorporate relation information via attentive interactions between the knowledge graph and its dual relation counterpart, and further capture neighboring structures to learn better entity representations. Experiments on three real-world cross-lingual datasets show that our approach delivers better and more robust results over the state-of-the-art alignment methods by learning better KG representations.

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