Author name cluster

Xiaoke Hao

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.

3 papers

1 author row

JBHI Journal 2025 Journal Article

A Hierarchical Graph Convolutional Network With Infomax-Guided Graph Embedding for Population-Based ASD Detection

Xiaoke Hao
Mingming Ma
Jiaqing Tao
Jiahui Cao
Jing Qin
Feng Liu
Daoqiang Zhang
Dong Ming

Recently, functional magnetic resonance imaging (fMRI)-based brain networks have been shown to be an effective diagnostic tool with great potential for accurately detecting autism spectrum disorders (ASD). Meanwhile, the successful use of graph convolution networks (GCNs) methods based on fMRI information has improved the classification accuracy of ASD. However, many graph convolution-based methods do not fully utilize the topological information of the brain functional connectivity network (BFCN) or ignore the effect of non-imaging information. Therefore, we propose a hierarchical graph embedding model that leverage both the topological information of the BFCN and the non-imaging information of the subjects to improve the classification accuracy. Specifically, our model first use the Infomax Module to automatically identify embedded features in regions of interests (ROIs) in the brain. Then, these features, along with non-imaging information, is used to construct a population graph model. Finally, we design a graph convolution framework to propagate and aggregate the node features and obtain the results for ASD detection. Our model takes into account both the significance of the BFCN to individual subjects and relationships between subjects in the population graph. The model performed autism detection using the Autism Brain Imaging Data Exchange (ABIDE) dataset and obtained an average accuracy of 77. 2% and an AUC of 87. 2%. These results exceed those of the baseline approach. Through extensive experiments, we demonstrate the competitiveness, robustness and effectiveness of our model in aiding ASD diagnosis.

Details DOI

AAAI Conference 2025 Conference Paper

Efficient Deformable Convolutional Prompt for Continual Test-Time Adaptation in Medical Image Segmentation

Shiyu Liu
Daoqiang Zhang
Xiaoke Hao

The domain gap resulting from mismatches in acquisition details like protocol and scanner between training and test data hinders the deployment of the trained model in clinical practice. To address this issue, Continual test-time adaptation (CTTA) has been proposed to adapt the source model to continually changing unlabeled domains without accessing the source data. Existing methods learn an image-level visual prompt for target domains and inject the trainable prompt into the input space. However, they either combine the input with a prompt of equal scale or determine the prompt injection position through complex strategies such as uncertainty estimation or Fourier Transform. These approaches substantially increase the number of trainable parameters and computational burden, especially in high-dimensional medical imaging data. To overcome these challenges, we propose the Efficient Deformable Convolutional Prompt (EDCP), which leverages the inductive bias of convolution to reduce trainable parameters compared to standard prompts. We further enhance convolution by making it deformable, addressing fine-grained domain shifts at the pixel level through an offset branch. To improve training efficiency and balance parameters between the convolution and offset branches, we decompose the offset transformation into two parts, storing one in an offset bank that also serves as a domain indicator. This bank accelerates training by skipping test images similar to those already stored. Prompt updates are guided by layer-wise alignment of source-target statistics without unfreezing batch normalization layers. Extensive experiments demonstrate the superiority of our method in 2D and 3D medical image segmentation tasks.

PDF Details DOI

JBHI Journal 2019 Journal Article

Identifying Resting-State Multifrequency Biomarkers via Tree-Guided Group Sparse Learning for Schizophrenia Classification

Jiashuang Huang
Qi Zhu
Xiaoke Hao
XiaoMeng Shi
Shuzhan Gao
Xijia Xu
Daoqiang Zhang

The fractional amplitude of low-frequency fluctuations (fALFF) has been widely used as potential clinical biomarkers for resting-state functional-magnetic-resonance-imaging-based schizophrenia diagnosis. How-ever, previous studies usually measure the fALFF with specific bands from 0. 01 to 0. 08 Hz, which cannot fully delineate the complex variations of spontaneous fluctuations in the resting-state brain. In addition, fALFF data are intrinsically constrained by the brain structure, but most of the traditional methods have not consider it in feature selection. For addressing these problems, we propose a model to classify schizophrenia in multifrequency bands with tree-guided group sparse learning. In detail, we first acquire the fALFF data in multifrequency bands (i. e. , slow-5: 0. 01-0. 027 Hz, slow-4: 0. 027-0. 073 Hz, slow-3: 0. 073-0. 198 Hz, and slow-2: 0. 198-0. 25 Hz). Then, we divide the whole brain into different candidate patches and select those significant patches related to schizophrenia using random forest-based important score. Moreover, we use tree-structured sparse learning method for feature selection with the above patch spatial constraint. Finally, considering biomarkers from multifrequency bands can reflect complementary information among multiple-frequency bands, we adopt the multikernel learning method to combine features of multifrequency bands for classification. Our experimental results show that these biomarkers from multifrequency bands can achieve a classification accuracy of 91. 1% on 17 schizophrenia patients and 17 healthy controls, further demonstrating that the multifrequency bands analysis can better account for classification of schizophrenia.

Details DOI