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2026

Artifact-suppressed 3D retinal microvascular segmentation via multi-scale topology regulation

Luo, T., Zhang, J., Chen, T., He, Z., Meng, Y., Liu, M., . . . Zhang, D. (2026). Artifact-suppressed 3D retinal microvascular segmentation via multi-scale topology regulation. MEDICAL IMAGE ANALYSIS, 110. doi:10.1016/j.media.2026.103988

DOI
10.1016/j.media.2026.103988
Journal article

DFuse-Net: Disentangled feature fusion with uncertainty-aware learning for reliable multi-modal brain tumor segmentation

Zhou, T., Wang, Z., Ruan, S., Meng, Y., Duan, J., & Lei, B. (2026). DFuse-Net: Disentangled feature fusion with uncertainty-aware learning for reliable multi-modal brain tumor segmentation. MEDICAL IMAGE ANALYSIS, 109. doi:10.1016/j.media.2025.103923

DOI
10.1016/j.media.2025.103923
Journal article

GLCP: Global-to-Local Connectivity Preservation for Tubular Structure Segmentation

Zhou, F., Gao, Z., Zhao, H., Xie, J., Meng, Y., Zhao, Y., . . . Zheng, Y. (2026). GLCP: Global-to-Local Connectivity Preservation for Tubular Structure Segmentation. MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2025, PT XVI, 15975, 237-247. doi:10.1007/978-3-032-05325-1_23

DOI
10.1007/978-3-032-05325-1_23
Journal article

StealthMark: Harmless and Stealthy Ownership Verification for Medical Segmentation via Uncertainty-Guided Backdoors

Yu, Q., Zhang, C., Jin, G., Huang, T., Zhou, W., Li, W., . . . Meng, Y. (2026). StealthMark: Harmless and Stealthy Ownership Verification for Medical Segmentation via Uncertainty-Guided Backdoors. IEEE TRANSACTIONS ON IMAGE PROCESSING, 35, 1290-1304. doi:10.1109/TIP.2026.3655563

DOI
10.1109/TIP.2026.3655563
Journal article

2025

Uncertainty Quantification for Multiple-Choice Questions is Just One-Token Deep

Zeng, Q., Jin, M., Yu, Q., Wang, Z., Hua, W., Sun, G., . . . Zhang, Y. (2025). Uncertainty Quantification for Multiple-Choice Questions is Just One-Token Deep. In Proceedings of the 34th ACM International Conference on Information and Knowledge Management (pp. 5474-5478). ACM. doi:10.1145/3746252.3760887

DOI
10.1145/3746252.3760887
Conference Paper

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers

DOI
10.48550/arxiv.2504.15928
Preprint

Are Spatial-Temporal Graph Convolution Networks for Human Action Recognition Over-Parameterized?

DOI
10.48550/arxiv.2505.10679
Preprint

Incomplete Modality Disentangled Representation for Ophthalmic Disease Grading and Diagnosis

Liu, C., Huang, Z., Chen, Z., Tang, F., Tian, Y., Xu, Z., . . . Meng, Y. (2025). Incomplete Modality Disentangled Representation for Ophthalmic Disease Grading and Diagnosis. In THIRTY-NINTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, AAAI-25, VOL 39 NO 5 (pp. 5361-5369). Retrieved from https://www.webofscience.com/

Conference Paper

Enhancing Diagnostic Accuracy in Rare and Common Fundus Diseases with a Knowledge-Rich Vision-Language Model

DOI
10.48550/arxiv.2406.09317
Preprint

Incomplete Modality Disentangled Representation for Ophthalmic Disease Grading and Diagnosis

DOI
10.48550/arxiv.2502.11724
Preprint

Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers?

DOI
10.48550/arxiv.2404.07066
Preprint

Self-adaptive vision-language model for 3D segmentation of pulmonary artery and vein

DOI
10.48550/arxiv.2501.03722
Preprint

Excavate the Potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement

Cheng, Z., Wang, W., Chen, G. -Y., Ju, Y., Cheng, Y., Liu, Z., . . . Song, J. (2025). Excavate the Potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 18, 27163-27176. doi:10.1109/JSTARS.2025.3621304

DOI
10.1109/JSTARS.2025.3621304
Journal article

Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concepts at Different Layers?

Jin, M., Yu, Q., Huang, J., Zeng, Q., Wang, Z., Hua, W., . . . Zhang, Y. (2025). Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concepts at Different Layers?. In Proceedings International Conference on Computational Linguistics Coling (pp. 558-573).

Conference Paper

MR<SUP>2</SUP>-Net: Retinal OCTA Image Stitching via Multi-Scale Representation Learning and Dynamic Location Guidance

Mao, H., Ma, Y., Zhang, D., Meng, Y., Ma, S., Qiao, Y., . . . Zhang, J. (2025). MR<SUP>2</SUP>-Net: Retinal OCTA Image Stitching via Multi-Scale Representation Learning and Dynamic Location Guidance. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 29(1), 482-494. doi:10.1109/JBHI.2024.3467256

DOI
10.1109/JBHI.2024.3467256
Journal article

Randomness-Restricted Diffusion Model for Ocular Surface Structure Segmentation

Guo, X., Wen, H., Hao, H., Zhao, Y., Meng, Y., Liu, J., . . . Zhao, Y. (2025). Randomness-Restricted Diffusion Model for Ocular Surface Structure Segmentation. IEEE TRANSACTIONS ON MEDICAL IMAGING, 44(3), 1359-1372. doi:10.1109/TMI.2024.3494762

DOI
10.1109/TMI.2024.3494762
Journal article

Super-Resolution Reconstruction of OCTA Via Multi-Field-of-View Representation Learning.

Hao, H., Leng, S., Meng, Y., Liu, Y., Zheng, Y., Fu, H., . . . Zhao, Y. (2025). Super-Resolution Reconstruction of OCTA Via Multi-Field-of-View Representation Learning.. IEEE journal of biomedical and health informatics, PP. doi:10.1109/jbhi.2025.3593149

DOI
10.1109/jbhi.2025.3593149
Journal article

2024

Artificial intelligence-based classification of cardiac autonomic neuropathy from retinal fundus images in patients with diabetes: The Silesia Diabetes Heart Study

Nabrdalik, K., Irlik, K., Meng, Y., Kwiendacz, H., Piasnik, J., Hendel, M., . . . Alam, U. (2024). Artificial intelligence-based classification of cardiac autonomic neuropathy from retinal fundus images in patients with diabetes: The Silesia Diabetes Heart Study. CARDIOVASCULAR DIABETOLOGY, 23(1). doi:10.1186/s12933-024-02367-z

DOI
10.1186/s12933-024-02367-z
Journal article

CLIP-DR: Textual Knowledge-Guided Diabetic Retinopathy Grading with Ranking-aware Prompting

DOI
10.48550/arxiv.2407.04068
Preprint

Multi-granularity learning of explicit geometric constraint and contrast for label-efficient medical image segmentation and differentiable clinical function assessment.

Meng, Y., Zhang, Y., Xie, J., Duan, J., Joddrell, M., Madhusudhan, S., . . . Zheng, Y. (2024). Multi-granularity learning of explicit geometric constraint and contrast for label-efficient medical image segmentation and differentiable clinical function assessment.. Medical image analysis, 95, 103183. doi:10.1016/j.media.2024.103183

DOI
10.1016/j.media.2024.103183
Journal article

AI-driven generalized polynomial transformation models for unsupervised fundus image registration

Chen, X., Fan, X., Meng, Y., & Zheng, Y. (2024). AI-driven generalized polynomial transformation models for unsupervised fundus image registration. FRONTIERS IN MEDICINE, 11. doi:10.3389/fmed.2024.1421439

DOI
10.3389/fmed.2024.1421439
Journal article

CLIP-DR: Textual Knowledge-Guided Diabetic Retinopathy Grading with Ranking-Aware Prompting

Yu, Q., Xie, J., Anh, N., Zhao, H., Zhang, J., Fu, H., . . . Meng, Y. (2024). CLIP-DR: Textual Knowledge-Guided Diabetic Retinopathy Grading with Ranking-Aware Prompting. In Unknown Book (Vol. 15001, pp. 667-677). doi:10.1007/978-3-031-72378-0_62

DOI
10.1007/978-3-031-72378-0_62
Chapter

Clinical Insight-Augmented Multi-View Learning for Alzheimer’s Detection in Retinal OCTA Images

Zhang, Y., Hao, J., Zheng, B., Liu, Y., Meng, Y., Zhang, J., . . . Zhao, Y. (2024). Clinical Insight-Augmented Multi-View Learning for Alzheimer’s Detection in Retinal OCTA Images. In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 2900-2907). IEEE. doi:10.1109/bibm62325.2024.10822671

DOI
10.1109/bibm62325.2024.10822671
Conference Paper

Multi-disease Detection in Retinal Images Guided by Disease Causal Estimation

Xie, J., Chen, X., Zhao, Y., Meng, Y., Zhao, H., Anh, N., . . . Zheng, Y. (2024). Multi-disease Detection in Retinal Images Guided by Disease Causal Estimation. In Unknown Book (Vol. 15001, pp. 743-753). doi:10.1007/978-3-031-72378-0_69

DOI
10.1007/978-3-031-72378-0_69
Chapter

Self-Guided Adversarial Network for Domain Adaptive Retinal Layer Segmentation

Zhang, J., Lu, C., Song, R., Zheng, Y., Hao, H., Meng, Y., & Zhao, Y. (2024). Self-Guided Adversarial Network for Domain Adaptive Retinal Layer Segmentation. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 73. doi:10.1109/TIM.2024.3440388

DOI
10.1109/TIM.2024.3440388
Journal article

The Impact of Reasoning Step Length on Large Language Models

Jin, M., Yu, Q., Dong, S., Zhao, H., Hua, W., Meng, Y., . . . Du, M. (2024). The Impact of Reasoning Step Length on Large Language Models. In FINDINGS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: ACL 2024 (pp. 1830-1842). Retrieved from https://www.webofscience.com/

Conference Paper

2023

Weakly Supervised Segmentation with Point Annotations for Histopathology Images via Contrast-Based Variational Model

Zhang, H., Burrows, L., Meng, Y., Sculthorpe, D., Mukherjee, A., Coupland, S. E., . . . Zheng, Y. (2023). Weakly Supervised Segmentation with Point Annotations for Histopathology Images via Contrast-Based Variational Model. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE. doi:10.1109/cvpr52729.2023.01500

DOI
10.1109/cvpr52729.2023.01500
Conference Paper

Corneal Confocal Microscopy Based Deep Learning Algorithms Demonstrate Excellent Diagnostic Accuracy in Identifying Patients with Dementia, Multiple Sclerosis and Parkinson's Disease

DOI
10.2139/ssrn.4346776
Preprint

2022

Informed Consent In Facial Photograph Publishing: A Cross-sectional Pilot Study To Determine The Effectiveness Of Deidentification Methods

Preston, F. G., Meng, Y., Zheng, Y., Hsuan, J., Hamill, K. J., & McCormick, A. G. (2022). Informed Consent In Facial Photograph Publishing: A Cross-sectional Pilot Study To Determine The Effectiveness Of Deidentification Methods. JOURNAL OF EMPIRICAL RESEARCH ON HUMAN RESEARCH ETHICS, 17(3), 373-381. doi:10.1177/15562646221075459

DOI
10.1177/15562646221075459
Journal article

DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification

DOI
10.48550/arxiv.2203.12081
Preprint

DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification

Zhang, H., Meng, Y., Zhao, Y., Qiao, Y., Yang, X., Coupland, S. E., & Zheng, Y. (2022). DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification. In 2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022) (pp. 18780-18790). doi:10.1109/CVPR52688.2022.01824

DOI
10.1109/CVPR52688.2022.01824
Conference Paper

DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification.

Zhang, H., Meng, Y., Zhao, Y., Qiao, Y., Yang, X., Coupland, S. E., & Zheng, Y. (2022). DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification.. CoRR, abs/2203.12081.

Journal article

Diagnosis of Diabetic Neuropathy by Artificial Intelligence using Corneal Confocal Microscopy

Meng, Y., Ferdousi, M., Petropoulos, I. N., Malik, R. A., Zhao, Y., Alam, U., & Zheng, Y. (2022). Diagnosis of Diabetic Neuropathy by Artificial Intelligence using Corneal Confocal Microscopy. In EUROPEAN JOURNAL OF OPHTHALMOLOGY Vol. 32 (pp. 11-12). Retrieved from https://www.webofscience.com/

Conference Paper

2021

BI-GCN: Boundary-Aware Input-Dependent Graph Convolution Network for Biomedical Image Segmentation

DOI
10.48550/arxiv.2110.14775
Preprint

A regularization term for slide correlation reduction in whole slide image analysis with deep learning.

Zhang, H., Meng, Y., Qian, X., Yang, X., Coupland, S. E., & Zheng, Y. (2021). A regularization term for slide correlation reduction in whole slide image analysis with deep learning.. In M. P. Heinrich, Q. Dou, M. D. Bruijne, J. Lellmann, A. Schlaefer, & F. Ernst (Eds.), MIDL Vol. 143 (pp. 842-854). PMLR. Retrieved from http://proceedings.mlr.press/v143/

Conference Paper

BI-GCN: Boundary-Aware Input-Dependent Graph Convolution Network for Biomedical Image Segmentation

Meng, Y., Zhang, H., Gao, D., Zhao, Y., Yang, X., Qian, X., . . . Zheng, Y. (2021). BI-GCN: Boundary-Aware Input-Dependent Graph Convolution Network for Biomedical Image Segmentation. In 32nd British Machine Vision Conference Bmvc 2021.

Conference Paper

2020

Undated

Corneal Confocal Microscopy Based Deep Learning Algorithms Demonstrate Excellent Diagnostic Accuracy in Identifying Patients with Dementia, Multiple Sclerosis and Parkinson's Disease

Meng, Y., Anson, M., Preston, F., Burgess, J., Ferdousi, M., Silverdale, M., . . . Alam, U. (n.d.). Corneal Confocal Microscopy Based Deep Learning Algorithms Demonstrate Excellent Diagnostic Accuracy in Identifying Patients with Dementia, Multiple Sclerosis and Parkinson's Disease.

Journal article