Under Review
VTRL-Med: Vision-Topology Reinforced Learning for Medical Image Clustering
Guangyu Meng, Pengfei Gu, John P. Lalor, Peixian Liang, Erin W. Chambers, Danny Z. Chen
Under review at CVPR 2026
A novel visual-structural and topology-aware framework that achieves state-of-the-art performance with 81.51% average accuracy across 13 medical benchmarks, representing a 4.48% improvement over existing baselines while nearly matching supervised methods in a fully unsupervised setting. The framework adapts SAM with mask-based structural priors and persistent homology features to enable anatomically consistent clustering.
TopoCL: Topology-Enhanced Contrastive Learning for Medical Image Analysis
Guangyu Meng, Pengfei Gu, John P. Lalor, Peixian Liang, Erin W. Chambers, Danny Z. Chen
Under review at CVPR 2026
A universal framework that demonstrates consistent accuracy improvements averaging +3.26% with strong statistical significance (80% of comparisons with p<0.001) across five state-of-the-art contrastive learning methods (SimCLR, MoCo-v3, BYOL, DINO, and Barlow Twins) on five medical imaging datasets. Features topology-aware augmentations, a Hierarchical Topology Encoder, and an adaptive mixture-of-experts fusion module.
A Stable and Theoretically Grounded Gromov-Wasserstein Distance for Reeb Graph Comparison using Persistence Images
Guangyu Meng, Erin W. Chambers
Under review at Symposium on Computational Geometry (SOCG) 2026
A stable algorithmic framework for comparing Reeb graphs using Gromov-Wasserstein distance with persistence images, accompanied by rigorous theoretical stability proofs under scalar field perturbations. This work provides theoretical guarantees for topological shape analysis and comparison in computational geometry applications.
Journal Articles
Psychology-based Unified Dynamic Framework for Curriculum Learning
Guangyu Meng, Qingkai Zeng, John P. Lalor, Hong Yu
Computational Linguistics Journal, 2025
A psychology-based curriculum learning framework that applies Item Response Theory to artificial crowds for model-independent difficulty quantification. Demonstrated effectiveness on both classification and generation tasks, achieving 69.68% faster training and 75.48% speedup over state-of-the-art methods on large language model fine-tuning.
Cell Instance Segmentation: The Devil Is in the Boundaries
Peixian Liang, Yifan Ding, Yizhe Zhang, Jianxu Chen, Hao Zheng, Hongxiao Wang, Yejia Zhang, Guangyu Meng, Tim Weninger, Michael Niemier, X. Sharon Hu, Danny Z. Chen
IEEE Transactions on Medical Imaging (TMI), 2025
Novel approach for cell instance segmentation focusing on boundary detection and refinement in medical imaging applications.
Efficient Approximation of Earth Mover's Distance Based on Nearest Neighbor Search
Guangyu Meng, Ruyu Zhou, Liu Liu, Peixian Liang, Fang Liu, Danny Z. Chen, Michael Niemier, X. Sharon Hu
IEEE Transactions on Multimedia (TMM), 2025
A GPU-accelerated Nearest Neighbor Search approximation algorithm achieving 44× to 135× speedup over exact Earth Mover's Distance computation while maintaining superior accuracy (91.88% on 20news, 93.24% on Amazon reviews). Includes comprehensive theoretical analysis of time complexity and error bounds, enabling scalable optimal transport for large-scale computer vision applications.