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Yanda Meng

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5 papers

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JBHI Journal 2025 Journal Article

$\text{MR}^{2}$-Net: Retinal OCTA Image Stitching via Multi-Scale Representation Learning and Dynamic Location Guidance

Haiting Mao
Yuhui Ma
Dan Zhang
Yanda Meng
Shaodong Ma
Yuchuan Qiao
Huazhu Fu
Caifeng Shan

Optical coherence tomography angiography (OCTA) plays a crucial role in quantifying and analyzing retinal vascular diseases. However, the limited field of view (FOV) inherent in most commercial OCTA imaging systems poses a significant challenge for clinicians, restricting the possibility to analyze larger retinal regions of high resolution. Automatic stitching of OCTA scans in adjacent regions may provide a promising solution to extend the region of interest. However, commonly-used stitching algorithms face difficulties in achieving effective alignment due to noise, artifacts and dense vasculature present in OCTA images. To address these challenges, we propose a novel retinal OCTA image stitching network, named $\text{MR}^{2}$ -Net, which integrates multi-scale representation learning and dynamic location guidance. In the first stage, an image registration network with a progressive multi-resolution feature fusion is proposed to derive deep semantic information effectively. Additionally, we introduce a dynamic guidance strategy to locate the foveal avascular zone (FAZ) and constrain registration errors in overlapping vascular regions. In the second stage, an image fusion network based on multiple mask constraints and adjacent image aggregation (AIA) strategies is developed to further eliminate the artifacts in the overlapping areas of stitched images, thereby achieving precise vessel alignment. To validate the effectiveness of our method, we conduct a series of experiments on two delicately constructed datasets, i. e. , OPTOVUE-OCTA and SVision-OCTA. Experimental results demonstrate that our method outperforms other image stitching methods and effectively generates high-quality wide-field OCTA images, achieving a structural similarity index (SSIM) score of 0. 8264 and 0. 8014 on the two datasets, respectively.

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AAAI Conference 2025 Conference Paper

Incomplete Modality Disentangled Representation for Ophthalmic Disease Grading and Diagnosis

Chengzhi Liu
Zile Huang
Zhe Chen
Feilong Tang
Yu Tian
Zhongxing Xu
Zihong Luo
Yalin Zheng

Ophthalmologists typically require multimodal data sources to improve diagnostic accuracy in clinical decisions. However, due to medical device shortages, low-quality data and data privacy concerns, missing data modalities are common in real-world scenarios. Existing deep learning methods tend to address it by learning an implicit latent subspace representation for different modality combinations. We identify two significant limitations of these methods: (1) implicit representation constraints that hinder the model's ability to capture modality-specific information and (2) modality heterogeneity, causing distribution gaps and redundancy in feature representations. To address these, we propose an Incomplete Modality Disentangled Representation (IMDR) strategy, which disentangles features into explicit independent modal-common and modal-specific features by guidance of mutual information, distilling informative knowledge and enabling it to reconstruct valuable missing semantics and produce robust multimodal representations. Furthermore, we introduce a joint proxy learning module that assists IMDR in eliminating intra-modality redundancy by exploiting the extracted proxies from each class. Experiments on four ophthalmology multimodal datasets demonstrate that the proposed IMDR outperforms the state-of-the-art methods significantly.

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JBHI Journal 2025 Journal Article

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

Huaying Hao
Shaoyi Leng
Yanda Meng
Yonghuai Liu
Yalin Zheng
Huazhu Fu
Jiong Zhang
Quanyong Yi

High-resolution Optical Coherence Tomography Angiography (OCTA) images are essential for morphological analysis and biomarker measurement of the retinal vasculature. They can also provide underlying biomarkers for the accurate analysis of eye-related diseases. The trade-off between the high resolution (HR) and large scanning field-of-view (FOV) is a long-standing problem for OCTA image instrument. A large FOV image provides more retinal information with shorter acquisition time but often suffers from low resolution (LR), high scatter noise, and poor vascular contrast. In order to obtain HR OCTA images with larger FOV, we propose a novel self-similar dynamic domain adaptation network based on cross-field-of-view representation learning. The network enables LR images ( i. e. , $6\times \text{6}~\text{mm}^{2}$ ) to learn HR image ( i. e. , $3\times \text{3}~\text{mm}^{2}$ ) feature representations specialized for OCTA by constructing feature mapping relations for cross-field-of-view OCTA scans. To be specific, a multiple random degradation model is proposed on HR images to generate various synthetic LR images. Further, we propose a dynamic domain adaptation framework that prompts feature dynamic alignment of the LR image reconstruction results with those of synthetic LR images. Finally, a novel self-similar supervision loss is proposed to optimize the reconstruction results from LR to HR by exploiting the similarity between vessels in different regions. Experimental results on three OCTA datasets show that the proposed method surpasses existing state-of-the-art ones, significantly enhancing retinal structure segmentation and disease classification. Our OCTA dataset (the first dataset in this research area with paired $3\times 3$ and $6\times \text{6}~\text{mm}^{2}$ OCTA images) and code are publicly available.

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AAAI Conference 2024 Conference Paper

Dynamic Semantic-Based Spatial Graph Convolution Network for Skeleton-Based Human Action Recognition

Jianyang Xie
Yanda Meng
Yitian Zhao
Anh Nguyen
Xiaoyun Yang
Yalin Zheng

Graph convolutional networks (GCNs) have attracted great attention and achieved remarkable performance in skeleton-based action recognition. However, most of the previous works are designed to refine skeleton topology without considering the types of different joints and edges, making them infeasible to represent the semantic information. In this paper, we proposed a dynamic semantic-based graph convolution network (DS-GCN) for skeleton-based human action recognition, where the joints and edge types were encoded in the skeleton topology in an implicit way. Specifically, two semantic modules, the joints type-aware adaptive topology and the edge type-aware adaptive topology, were proposed. Combining proposed semantics modules with temporal convolution, a powerful framework named DS-GCN was developed for skeleton-based action recognition. Extensive experiments in two datasets, NTU-RGB+D and Kinetics-400 show that the proposed semantic modules were generalized enough to be utilized in various backbones for boosting recognition accuracy. Meanwhile, the proposed DS-GCN notably outperformed state-of-the-art methods. The code is released here https://github.com/davelailai/DS-GCN

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JBHI Journal 2020 Journal Article

Introducing the GEV Activation Function for Highly Unbalanced Data to Develop COVID-19 Diagnostic Models

Joshua Bridge
Yanda Meng
Yitian Zhao
Yong Du
Mingfeng Zhao
Renrong Sun
Yalin Zheng

Fast and accurate diagnosis is essential for the efficient and effective control of the COVID-19 pandemic that is currently disrupting the whole world. Despite the prevalence of the COVID-19 outbreak, relatively few diagnostic images are openly available to develop automatic diagnosis algorithms. Traditional deep learning methods often struggle when data is highly unbalanced with many cases in one class and only a few cases in another; new methods must be developed to overcome this challenge. We propose a novel activation function based on the generalized extreme value (GEV) distribution from extreme value theory, which improves performance over the traditional sigmoid activation function when one class significantly outweighs the other. We demonstrate the proposed activation function on a publicly available dataset and externally validate on a dataset consisting of 1, 909 healthy chest X-rays and 84 COVID-19 X-rays. The proposed method achieves an improved area under the receiver operating characteristic (DeLong's p-value < 0. 05) compared to the sigmoid activation. Our method is also demonstrated on a dataset of healthy and pneumonia vs. COVID-19 X-rays and a set of computerized tomography images, achieving improved sensitivity. The proposed GEV activation function significantly improves upon the previously used sigmoid activation for binary classification. This new paradigm is expected to play a significant role in the fight against COVID-19 and other diseases, with relatively few training cases available.

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