Posts by Collection

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Inversion and Prediction of Ocean Resource Flow

Published in Academic Journal of Environment & Earth Science, 2020

Studies inversion and forecasting of ocean resource flow using data-driven modeling to support marine resource planning.

Recommended citation: H. Li, X. Gao, Y. Zhao, M. Xu, “Inversion and Prediction of Ocean Resource Flow,” Academic Journal of Environment & Earth Science, 2020.

Research on Application of Computer Image Processing in Web Design

Published in IEEE ICPICS 2020, 2020

Applies computer vision techniques to enhance web design aesthetics and layout generation in the ICPICS 2020 proceedings.

Recommended citation: Y. Zheng, H. Li, A. Ren, “Research on Application of Computer Image Processing in Web Design,” IEEE ICPICS 2020, pp. 405-409, doi:10.1109/ICPICS50287.2020.9202235.

MetaScleraSeg: An Effective Meta Learning Framework for Generalized Sclera Segmentation

Published in Neural Computing and Applications, 2023

Meta-learning framework that improves cross-dataset generalization for sclera segmentation with limited labeled data.

Recommended citation: H. Li (co-first), C. Wang, W. Ma, G. Zhao, Z. He, “MetaScleraSeg: An Effective Meta Learning Framework for Generalized Sclera Segmentation,” Neural Computing and Applications, 2023.

Sclera-TransFuse: Fusing Swin Transformer and CNN for Accurate Sclera Segmentation

Published in IEEE International Joint Conference on Biometrics (IJCB), 2023

Two-stream Transformer+CNN framework for sclera segmentation under unconstrained conditions; awarded Best Student Paper at IJCB 2023.

Recommended citation: H. Li, C. Wang, G. Zhao, Z. He, Y. Wang, Z. Sun, “Sclera-TransFuse: Fusing Swin Transformer and CNN for Accurate Sclera Segmentation,” IJCB 2023. Best Student Paper Award.

Sclera Segmentation and Joint Recognition Benchmarking Competition: SSRBC 2023

Published in IEEE International Joint Conference on Biometrics (IJCB), 2023

Benchmark report summarizing tasks, datasets, baselines, and winning methods for the 8th SSRBC competition at IJCB 2023.

Recommended citation: A. Das, S. Atreya, A. Mukherjee, M. Vitek, H. Li, et al., “Sclera Segmentation and Joint Recognition Benchmarking Competition: SSRBC 2023,” IJCB 2023.

OcularSeg: Accurate and Efficient Multi-Modal Ocular Segmentation in Non-Constrained Scenarios

Published in Electronics, 13(10):1967, 2024

Multi-modal ocular segmentation approach that handles non-constrained capture conditions while remaining efficient for deployment.

Recommended citation: Y. Zhang, C. Wang, H. Li, et al., “OcularSeg: Accurate and Efficient Multi-Modal Ocular Segmentation in Non-Constrained Scenarios,” Electronics, 2024.

Sclera-TransFuse: Fusing Swin Transformer and CNN for Accurate Sclera Segmentation and Recognition

Published in IEEE Transactions on Biometrics, Behavior, and Identity Science (TBIOM), 2024

Sclera segmentation and recognition with a hybrid Swin Transformer + CNN two-stream design that captures global context while preserving fine ocular details, improving robustness in unconstrained scenarios.

Recommended citation: C. Wang, H. Li*, G. Zhao, Z. He, Y. Wang, Z. Sun, “Sclera-TransFuse: Fusing Swin Transformer and CNN for Accurate Sclera Segmentation and Recognition,” IEEE TBIOM, 2024.

MLLM4PUE: Toward Universal Embeddings in Digital Pathology Through Multimodal LLMs

Published in arXiv preprint arXiv:2502.07221, 2025

Explores multimodal large language models to learn universal embeddings for digital pathology, enhancing cross-domain generalization.

Recommended citation: Q. Zhou, T.M. Dang, W. Zhong, Y. Guo, H. Ma, S. Na, H. Li, J. Huang, “MLLM4PUE: Toward Universal Embeddings in Digital Pathology Through Multimodal LLMs,” arXiv:2502.07221, 2025.
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Leveraging Gait Patterns as Biomarkers: An Attention-Guided Deep Multiple Instance Learning Network for Scoliosis Classification

Published in arXiv preprint arXiv:2504.03894, 2025

Attention-guided deep multiple instance learning model that uses gait video to classify scoliosis without dense clinical labels.

Recommended citation: H. Li, Y. Guo, F. Jiang, Q. Zhou, H. Ma, J. Huang, “Leveraging Gait Patterns as Biomarkers: An Attention-Guided Deep Multiple Instance Learning Network for Scoliosis Classification,” arXiv:2504.03894, 2025.
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HAGE: Hierarchical Alignment Gene-Enhanced Pathology Representation Learning with Spatial Transcriptomics

Published in International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2025

Hierarchical alignment framework that fuses pathology images with spatial transcriptomics to enrich representations for downstream medical imaging tasks.

Recommended citation: T.M. Dang, H. Li, Y. Guo, H. Ma, F. Jiang, Y. Miao, Q. Zhou, J. Gao, J. Huang, “HAGE: Hierarchical Alignment Gene-Enhanced Pathology Representation Learning with Spatial Transcriptomics,” MICCAI 2025.

Text-Guided Multi-Instance Learning for Scoliosis Screening via Gait Video Analysis

Published in International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2025

Multi-instance learning approach that leverages text guidance on gait videos to screen scoliosis with reduced annotation effort.

Recommended citation: H. Li, Y. Guo, F. Jiang, T.M. Dang, H. Ma, Q. Zhou, J. Gao, J. Huang, “Text-Guided Multi-Instance Learning for Scoliosis Screening via Gait Video Analysis,” MICCAI 2025.

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.