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Dalian University of Technology

Academic institutionasia · cn
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Research library632linked papers
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Selected work

Representative Papers

M2SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation

Mar 20, 2023arXiv.org

Existing U-shaped medical image segmentation networks typically fuse multi-level features via element-wise addition or concatenation, which introduces redundancy and weakens cross-level complementarity, leading to inaccurate lesion localization and blurred boundaries. To address this, we propose the Multi-scale Intra–Multi-scale Subtraction Network (M2S-Net), featuring two key innovations: (1) a novel Multi-scale Intra-subtraction Unit (SU) and a pyramid-style cross-level multi-scale subtraction architecture that explicitly models complementary information through hierarchical differencing; and (2) a training-free LossNet that enables bottom-up, task-aware feature supervision. Evaluated across 11 cross-modality datasets—including colonoscopy, ultrasound, CT, and OCT—M2S-Net consistently outperforms state-of-the-art methods, achieving significant improvements in segmentation accuracy and boundary sharpness. This work establishes an efficient and interpretable paradigm for feature fusion in medical image segmentation.

23 citations3 influentialRead paper

Generative Multiform Bayesian Optimization

May 13, 2022IEEE Transactions on Cybernetics

Bayesian optimization (BO) of expensive black-box functions over complex input spaces—such as discrete or non-Euclidean domains—remains challenging. Existing generative BO (GBO) methods suffer from suboptimal convergence and solution quality due to reliance on a single latent space and inability to handle variable-dimensional inputs robustly. Method: We propose a multimodal generative BO framework featuring: (i) parallel optimization across multiple cooperative latent spaces; (ii) a generative model (VAE/GAN) with positive-correlation constraints to preserve fidelity between latent representations and objective values; and (iii) two cross-space information exchange strategies to reconcile the trade-off between dimension selection and the accuracy–convergence rate balance. Results: Evaluated on airfoil design, cantilever beam optimization, and area maximization tasks, our method achieves significantly faster convergence and higher-quality solutions than both single-latent-space GBO and conventional BO under limited evaluation budgets.

8 citations1 influentialRead paper

Learning Dynamic Collaborative Network for Semi-Supervised 3D Vessel Segmentation

Jun 10, 2025Computer Vision and Pattern Recognition

This work addresses a critical limitation in conventional semi-supervised 3D vessel segmentation methods, where fixed teacher–student roles often introduce cognitive bias due to suboptimal teacher performance, thereby constraining segmentation accuracy. To overcome this, the authors propose DiCo, a dynamic collaborative network that, for the first time, incorporates a role-switching mechanism between teacher and student during training. DiCo further integrates a multi-view ensemble module to emulate clinical multi-angle diagnosis and employs adversarial supervision on 2D projections to mitigate label inconsistency across views. Extensive experiments demonstrate that DiCo achieves state-of-the-art performance on three benchmark 3D vessel segmentation datasets, significantly enhancing the utilization efficiency of unlabeled data.

4 citations1 influentialRead paper

Interpretable Clustering: A Survey

Sep 01, 2024arXiv.org

High-stakes domains—such as healthcare and finance—demand interpretable clustering outcomes to ensure transparency, accountability, and regulatory compliance. Method: This survey systematically analyzes over 120 scholarly works, proposing the first unified taxonomy of interpretability dimensions for clustering. It rigorously distinguishes intrinsically interpretable models—including rule-based, prototype-based, and sparsity-driven approaches—from post-hoc explanation techniques—such as visualization, feature attribution, and local surrogate modeling. The study further develops a use-case-oriented, structured classification framework and principled evaluation criteria. Contribution/Results: It introduces the first practical guideline for selecting appropriate interpretable clustering methods based on application requirements. The work bridges theoretical foundations with real-world deployment, providing both conceptual clarity and actionable insights to support the development and adoption of clustering algorithms that jointly optimize accuracy and interpretability—thereby advancing trustworthy AI in ethically and regulatorily sensitive contexts.

4 citations1 influentialRead paper

Harder Is Better: Boosting Mathematical Reasoning via Difficulty-Aware GRPO and Multi-Aspect Question Reformulation

Jan 28, 2026

This work addresses the limitation of existing reinforcement learning approaches in mathematical reasoning, which often neglect challenging problems and lack a systematic mechanism for progressive difficulty escalation, thereby constraining model performance on complex tasks. To overcome this, the authors propose MathForge, a novel framework that jointly emphasizes high-difficulty problems from both algorithmic and data perspectives. Algorithmically, they introduce Difficulty-aware Grouped Policy Optimization (DGPO) with a difficulty-balanced advantage estimator. On the data side, they develop Multi-dimensional Question Rewriting (MQR), which enables controllable difficulty enhancement while preserving answer consistency. Extensive experiments demonstrate that MathForge significantly outperforms current methods across multiple mathematical reasoning benchmarks, validating the efficacy of a difficulty-centric training paradigm for enhancing large language models’ reasoning capabilities.

4 citationsRead paper
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