Author name cluster

Fa Zhang

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

8 papers

1 author row

JBHI Journal 2026 Journal Article

A VR-based Automated Strabismus Diagnosis System with Progressive Semi-Supervised Learning

Dehui Qiu
Bowei Ma
Ze Xiong
Yuhao Wang
Liguo Deng
Longfei Zhou
Xiaojie Cao
Weiwei Chen

Strabismus is a prevalent ocular disorder that can impair visual development and cause psychological issues if not diagnosed early. Conventional clinical diagnosis primarily relies on the prism cover test (PCT), which is subjective, requires patient cooperation, and lacks standardization. Recent advances in virtual reality (VR) and deep learning offer promising solutions for automated and standardized diagnosis. However, practical deployment faces three key challenges: realistic VR simulation of clinical exams, addressing image degradation (reflections/occlusions) with limited annotated data, and precise quantification of ocular deviations. In this study, we propose a novel VR-based automated strabismus diagnosis system by leveraging semi-supervised deep learning, and introduce a new clinical dataset, TongRenD. The framework incorporates five standardized clinical examination scenarios within a VR environment to ensure diagnostic consistency. We introduce ProgNet: an uncertainty-guided progressive semi-supervised segmentation network that integrates a Prototype-based Feature Representation Module (PFRM) to enhance robustness against visual noise and distortions under limited annotations. A dedicated 3D deviation estimation algorithm further enables accurate strabismus classification and angular measurement. Extensive experiments on the TongRenD and TEyeD datasets demonstrate that ProgNet outperforms state-of-the-art methods in segmentation accuracy. Clinical validation confirms that our system achieves high consistency with expert assessments, providing a standardized, non-invasive, and reliable solution for strabismus diagnosis.

Details DOI

AAAI Conference 2026 Conference Paper

Cyto-SSL: A Self-Supervised Pretraining Framework for Cytology Foundation Model

Yiming Zhang
Rui Yan
Xiaohua Wan
Yifan Zhao
Shuang Feng
Zhetao Xu
Ying Wang
Fa Zhang

Cytological images originate from exfoliated cells, collected via liquid-based slides and digitized into whole slide images (WSIs). Unlike histological WSIs that exhibit continuous and well-structured tissue, cytological WSIs are sparse in spatial distribution and unstructured in cellular relationships. Typically, the nucleus serves as the primary diagnostic feature, while surrounding cytoplasmic information plays a supportive role. These unique characteristics limit the development of effective foundation models and hinder the transferability of histology-based models for cytopathology. To address this, we propose **Cyto-SSL**, the first self-supervised pretraining framework for cytological images. It introduces **Nuclei-Centered Perturbation**, which highlights individual nuclei by perturbing non-nuclear regions. We also design an SR-Transformer module, which complements this by using sparse attention to concentrate on diagnostically relevant scattered cells, while iRPE helps model to capture local spatial relationships and avoids unnecessary attention to irrelevant global structures. Experimental results show that **Cyto-SSL** enhances performance across diverse cytological datasets and Multiple Instance Learning (MIL) methods. On a WSI-level dataset, it achieved 95.67% accuracy and outperformed ImageNet-pretrained ResNet-50 by 11.33%, demonstrating superior feature representation for cytological analysis. Additionally, **Cyto-SSL** modules are plug-and-play, easily integrated into other pretraining frameworks, yielding a 2.6% accuracy gain across different SSL methods.

PDF Details DOI

JBHI Journal 2026 Journal Article

MSFSNet: Multi-Source Few-Shot Adaptation Network for Cross-Subject Depression Recognition from EEG Signals

Kang Wang
Yanan Zhang
Yingwei Zhang
Fa Zhang
Jian Shen
Bin Hu

Depression is a prevalent mental disorder with severe socio-economic implications, and its early identification and intervention are crucial for mitigating disease progression. However, existing machine learning and deep learning-based approaches for depression recognition exhibit limited generalization across individuals, making them less adaptable to new subjects and restricting their practical applications. To address this issue, we propose a cross-subject depression recognition method based on Multi-Source Few-Shot Adaptation (MSFSA) using electroencephalography (EEG). The proposed method integrates multi-source domain adaptation and ensemble learning strategies. Specifically, the multi-source domain adaptation module employs an alternating training mechanism combining unsupervised domain adaptation and few-shot adaptation, reducing the model's dependency on specific subjects. Meanwhile, ensemble learning improves model robustness and stability by aggregating multiple model predictions, reducing the impact of individual model biases and enhancing classification reliability. Experiments were conducted on the public MODMA EEG dataset, comprising 53 subjects (24 patients with major depressive disorder and 29 healthy controls). With a theoretical chance level of 50% for the cross-subject classification setting, the results demonstrate that, compared with traditional machine learning methods, existing EEG-based depression recognition models, and advanced domain adaptation algorithms, leveraging the Alpha and low-Gamma band features as the key contributing factors, the proposed method achieves a significant improvement in accuracy, reaching 87. 12%, which outperforms the state-of-the-art HEMAsNet (80. 67%) and WDANet (70. 94%) on the same dataset under the 10-fold cross-subject validation protocol. These findings indicate that the proposed approach effectively reduces subject dependency in EEG-based depression recognition and provides a promising solution for improving cross-subject adaptability.

Details DOI

AAAI Conference 2026 Conference Paper

ST-LLM: Spatial Transcriptomics Embedding with Large Language Models

Zhetao Xu
Xiaohua Wan
Le Li
Shuang Feng
Yiming Zhang
Fa Zhang
Bin Hu

Spatial transcriptomics provides unprecedented opportunities to analyze gene patterns while preserving spatial tissue architecture. However, traditional deep learning methods for spatial transcriptomics analysis face significant challenges in multi-modal data integration, spatial dependency modeling, and biological knowledge incorporation, while existing large language models lack explicit spatial modeling capabilities for transcriptomic data. So we first present a Spatial Transcriptomics Embedding with Large Language Models (ST-LLM), a novel simple and effective approach that transforms intricate spatial graph structures into structured textual representations suitable for large language models (LLMs). ST-LLM dynamically constructs graph adjacency construction using reinforcement learning paradigms to adaptively optimize spatial relationships, converts the resulting graphs into hierarchical textual descriptions with spatial context, and leverages pre-trained semantic understanding to generate high-dimensional spatial-aware representations. Comprehensive experiments on 14 datasets demonstrate that ST-LLM achieves comparable or better performance than traditional model. ST-LLM shows that LLMs embeddings provide a new simple and effective path to encoding spatial transcriptomics biological knowledge.

PDF Details DOI

JBHI Journal 2025 Journal Article

Harmonic Wavelet Neural Network for Discovering Neuropathological Propagation Patterns in Alzheimer's Disease

Hongmin Cai
Ranran Deng
Defu Yang
Fa Zhang
Guorong Wu
Jiazhou Chen

Emerging researchindicates that the degenerative biomarkers associated with Alzheimer's disease (AD) exhibit a non-random distribution within the cerebral cortex, instead following the structural brain network. The alterations in brain networks occur much earlier than the onset of clinical symptoms, thereby affecting the progression of brain disease. In this context, the utilization of computational methods to ascertain the propagation patterns of neuropathological events would contribute to the comprehension of the pathophysiological mechanism involved in the evolution of AD. Despite the encouraging findings achieved by existing graph-based deep learning approaches in analyzing irregular graph data, their applications in identifying the spreading pathway of neuropathology are limited due to two disadvantages. They include (1) lack of a common brain network as an unbiased reference basis for group comparison, and (2) lack of an appropriate mechanism for the identification of propagation patterns. To this end, we propose a proof-of-concept harmonic wavelet neural network (HWNN) to predict the early stage of AD and localize disease-related significant wavelets, which can be used to characterize the spreading pathways of neuropathological events across the brain network. The extensive experiments constructed on both synthetic and real datasets demonstrate that our proposed method achieves superior performance in classification accuracy and statistical power of identifying propagation patterns, compared with other representative approaches.

Details DOI

JBHI Journal 2024 Journal Article

Guest Editorial Computational Mathematics Modeling in Cancer Analysis

Wenjian Qin
Tianming Liu
Fa Zhang

Cancer is a complex disease that can affect any body part. One key feature of cancer is the rapid production of abnormal cells that grow beyond their usual borders and can invade adjoining parts of the body and spread/metastasized to other organs. The process of metastasis is the crucial cause of cancer death. Environmental factors are a significant contributor to cancer initiation [1]. Numerous studies investigate various aspects of cancer, including pathogenesis, prevention, diagnosis, and treatment methods, with the goal of improving patient quality of life and increasing survival rates. Despite significant advances in the field, cancer continues to represent a global challenge for prevention and treatment.

Details DOI

JBHI Journal 2024 Journal Article

Sparse and Hierarchical Transformer for Survival Analysis on Whole Slide Images

Rui Yan
Zhilong Lv
Zhidong Yang
Senlin Lin
Chunhou Zheng
Fa Zhang

The Transformer-based methods provide a good opportunity for modeling the global context of gigapixel whole slide image (WSI), however, there are still two main problems in applying Transformer to WSI-based survival analysis task. First, the training data for survival analysis is limited, which makes the model prone to overfitting. This problem is even worse for Transformer-based models which require large-scale data to train. Second, WSI is of extremely high resolution (up to 150, 000 × 150, 000 pixels) and is typically organized as a multi-resolution pyramid. Vanilla Transformer cannot model the hierarchical structure of WSI (such as patch cluster-level relationships), which makes it incapable of learning hierarchical WSI representation. To address these problems, in this article, we propose a novel Sparse and Hierarchical Transformer (SH-Transformer) for survival analysis. Specifically, we introduce sparse self-attention to alleviate the overfitting problem, and propose a hierarchical Transformer structure to learn the hierarchical WSI representation. Experimental results based on three WSI datasets show that the proposed framework outperforms the state-of-the-art methods.

Details DOI

TCS Journal 2015 Journal Article

Randomized oblivious integral routing for minimizing power cost

Yangguang Shi
Fa Zhang
Jie Wu
Zhiyong Liu

Given an undirected network G ( V, E ) and a set of traffic requests R, the minimum power-cost routing problem requires that each R k ∈ R be routed along a single path to minimize ∑ e ∈ E ( l e ) α, where l e is the traffic load on edge e and α is a constant greater than 1. Typically, α ∈ ( 1, 3 ]. This problem is important in optimizing the energy consumption of networks. To address this problem, we propose a randomized oblivious routing algorithm. An oblivious routing algorithm makes decisions independently of the current traffic in the network. This feature enables the efficient implementation of our algorithm in a distributed manner, which is desirable for large-scale high-capacity networks. An important feature of our work is that our algorithm can satisfy the integral constraint, which requires that each traffic request R k should follow a single path. We prove that, given this constraint, no randomized oblivious routing algorithm can guarantee a competitive ratio bounded by o ( | E | α − 1 α + 1 ). By contrast, our approach provides a competitive ratio of O ( | E | α − 1 α + 1 log 2 α α + 1 ⁡ | V | ⋅ log α − 1 ⁡ D ), where D is the maximum demand of traffic requests. Furthermore, our results also hold for a more general case where the objective is to minimize ∑ e ( l e ) p, where p ≥ 1 is an arbitrary unknown parameter with a given upper bound α > 1. The theoretical results established in proving these bounds can be further generalized to a framework of designing and analyzing oblivious integral routing algorithms, which is significant for research on minimizing ∑ e ( l e ) α in specific scenarios with simplified problem settings. For instance, we prove that this framework can generate an oblivious integral routing algorithm whose competitive ratio can be bounded by O ( log α ⁡ | V | ⋅ log α − 1 ⁡ D ) and O ( log 3 α ⁡ | V | ⋅ log α − 1 ⁡ D ) on expanders and hypercubes, respectively.

Details DOI