Author name cluster

Ting Ma

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.

12 papers

1 author row

AAAI Conference 2026 Conference Paper

Medverse: A Universal Model for Full-Resolution 3D Medical Image Segmentation, Transformation and Enhancement

Jiesi Hu
Jianfeng Cao
Yanwu Yang
Chenfei Ye
Yixuan Zhang
Hanyang Peng
Ting Ma

In-context learning (ICL) offers a promising paradigm for universal medical image analysis, enabling models to perform diverse image processing tasks without retraining. However, current ICL models for medical imaging remain limited in two critical aspects: they cannot simultaneously achieve high-fidelity predictions and global anatomical understanding, and there is no unified model trained across diverse medical imaging tasks (e.g., segmentation and enhancement) and anatomical regions. As a result, the full potential of ICL in medical imaging remains underexplored. Thus, we present Medverse, a universal ICL model for 3D medical imaging, trained on 22 datasets covering diverse tasks in universal image segmentation, transformation, and enhancement across multiple organs, imaging modalities, and clinical centers. Medverse employs a next-scale autoregressive in-context learning framework that progressively refines predictions from coarse to fine, generating consistent, full-resolution volumetric outputs and enabling multi-scale anatomical awareness. We further propose a blockwise cross-attention module that facilitates long-range interactions between context and target inputs while preserving computational efficiency through spatial sparsity. Medverse is extensively evaluated on a broad collection of held-out datasets covering previously unseen clinical centers, organs, species, and imaging modalities. Results demonstrate that Medverse substantially outperforms existing ICL baselines and establishes a novel paradigm for in-context learning.

PDF Details DOI

JBHI Journal 2025 Journal Article

An EEG-Based Seizure Prediction Model Encoding Brain Network Temporal Dynamics

Jiahui Liao
Yiyi Chen
Yihang He
Kai Zhang
Ting Ma
Yilong Wang
Xiaoqiu Shao

EEG-based seizure prediction enables timely treatment for patients, but its performance is limited by the difficulty in effectively characterizing the temporal dynamics of epileptic brain networks. Metastability, which describes recurring topographical patterns of spontaneous neural activity over time, provides a unique perspective for capturing the dynamic evolution before seizure onset. In this study, we propose a seizure prediction model that fuses consistent epileptic network processes across subjects into a higher-order latent space. Specifically, we first construct metastable transition patterns to identify the recurrent network states over time. Through adversarial feature learning, we then impose the metastability prior on the latent embedding space encoded via a variational autoencoder (VAE), while leveraging the maximum mean discrepancy measure (MMD) to further mitigate the patient gap. The latent representation, endowed with physiological priors, is ultimately utilized for patient-independent seizure prediction. We evaluate our method on two publicly available and one clinical scalp EEG datasets. Compared to the existing methods, our method has improved AUC, sensitivity, and specificity on CHB-MIT dataset by approximately 9%, 5%, and 5%, respectively. Our method shows that combining brain network-based physiological prior with deep learning for EEG representation learning is a brand-new strategy for associating seizures with complex brain network variations, enabling reliable patient-independent seizure prediction.

Details DOI

JBHI Journal 2025 Journal Article

Hypercomplex Graph Neural Network: Towards Deep Intersection of Multi-Modal Brain Networks

Yanwu Yang
Chenfei Ye
Guoqing Cai
Kunru Song
Jintao Zhang
Yang Xiang
Ting Ma

The multi-modal neuroimage study has provided insights into understanding the heteromodal relationships between brain network organization and behavioral phenotypes. Integrating data from various modalities facilitates the characterization of the interplay among anatomical, functional, and physiological brain alterations or developments. Graph Neural Networks (GNNs) have recently become popular in analyzing and fusing multi-modal, graph-structured brain networks. However, effectively learning complementary representations from other modalities remains a significant challenge due to the sophisticated and heterogeneous inter-modal dependencies. Furthermore, most existing studies often focus on specific modalities (e. g. , only fMRI and DTI), which limits their scalability to other types of brain networks. To overcome these limitations, we propose a HyperComplex Graph Neural Network (HC-GNN) that models multi-modal networks as hypercomplex tensor graphs. In our approach, HC-GNN is conceptualized as a dynamic spatial graph, where the attentively learned inter-modal associations are represented as the adjacency matrix. HC-GNN leverages hypercomplex operations for inter-modal intersections through cross-embedding and cross-aggregation, enriching the deep coupling of multi-modal representations. We conduct a statistical analysis on the saliency maps to associate disease biomarkers. Extensive experiments on three datasets demonstrate the superior classification performance of our method and its strong scalability to various types of modalities. Our work presents a powerful paradigm for the study of multi-modal brain networks.

Details DOI

JBHI Journal 2024 Journal Article

A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentation

Jiesi Hu
Yanwu Yang
Xutao Guo
Ting Ma
Jinghua Wang

Source-free domain adaptation (SFDA) aims to adapt models trained on a labeled source domain to an unlabeled target domain without access to source data. In medical imaging scenarios, the practical significance of SFDA methods has been emphasized due to data heterogeneity and privacy concerns. Recent state-of-the-art SFDA methods primarily rely on self-training based on pseudo-labels (PLs). Unfortunately, the accuracy of PLs may deteriorate due to domain shift, thus limiting the effectiveness of the adaptation process. To address this issue, we propose a Chebyshev confidence guided SFDA framework to accurately assess the reliability of PLs and generate self-improving PLs for self-training. The Chebyshev confidence is estimated by calculating the probability lower bound of PL confidence, given the prediction and the corresponding uncertainty. Leveraging the Chebyshev confidence, we introduce two confidence-guided denoising methods: direct denoising and prototypical denoising. Additionally, we propose a novel teacher-student joint training scheme (TJTS) that incorporates a confidence weighting module to iteratively improve PLs' accuracy. The TJTS, in collaboration with the denoising methods, effectively prevents the propagation of noise and enhances the accuracy of PLs. Extensive experiments in diverse domain scenarios validate the effectiveness of our proposed framework and establish its superiority over state-of-the-art SFDA methods. Our paper contributes to the field of SFDA by providing a novel approach for precisely estimating the reliability of PLs and a framework for obtaining high-quality PLs, resulting in improved adaptation performance.

Details DOI

YNICL Journal 2024 Journal Article

Geodesic shape regression based deep learning segmentation for assessing longitudinal hippocampal atrophy in dementia progression

Na Gao
Hantao Chen
Xutao Guo
Xingyu Hao
Ting Ma

Longitudinal hippocampal atrophy is commonly used as progressive marker assisting clinical diagnose of dementia. However, precise quantification of the atrophy is limited by longitudinal segmentation errors resulting from MRI artifacts across multiple independent scans. To accurately segment the hippocampal morphology from longitudinal 3T T1-weighted MR images, we propose a diffeomorphic geodesic guided deep learning method called the GeoLongSeg to mitigate the longitudinal variabilities that unrelated to diseases by enhancing intra-individual morphological consistency. Specifically, we integrate geodesic shape regression, an evolutional model that estimates smooth deformation process of anatomical shapes, into a two-stage segmentation network. We adopt a 3D U-Net in the first-stage network with an enhanced attention mechanism for independent segmentation. Then, a hippocampal shape evolutional trajectory is estimated by geodesic shape regression and fed into the second network to refine the independent segmentation. We verify that GeoLongSeg outperforms other four state-of-the-art segmentation pipelines in longitudinal morphological consistency evaluated by test-retest reliability, variance ratio and atrophy trajectories. When assessing hippocampal atrophy in longitudinal data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), results based on GeoLongSeg exhibit spatial and temporal local atrophy in bilateral hippocampi of dementia patients. These features derived from GeoLongSeg segmentation exhibit the greatest discriminatory capability compared to the outcomes of other methods in distinguishing between patients and normal controls. Overall, GeoLongSeg provides an accurate and efficient segmentation network for extracting hippocampal morphology from longitudinal MR images, which assist precise atrophy measurement of the hippocampus in early stage of dementia.

Details DOI

JBHI Journal 2024 Journal Article

Manifold Learning-Based Common Spatial Pattern for EEG Signal Classification

Guoqing Cai
Fenghui Zhang
Bolun Yang
Shoulin Huang
Ting Ma

EEG signal classification using Riemannian manifolds has shown great potential. However, the huge computational cost associated with Riemannian metrics poses challenges for applying Riemannian methods, particularly in high-dimensional feature data. To address these, we propose an efficient ensemble method called MLCSP-TSE-MLP, which aims to reduce the computational cost while achieving superior performance. MLCSP of the ensemble utilizes a Riemannian graph embedding strategy to learn intrinsic low-dimensional sub-manifolds, enhancing discrimination. TSE uses the Euclidean mean as the reference point for tangent space mapping and reducing computational cost. Finally, the ensemble incorporates the MLP classifier to offer improved classification performance. Classification results conducted on three datasets demonstrate that MLCSP-TSE-MLP achieves significant superior performance compared to various competing methods. Notably, the MLCSP-TSE module achieves a remarkable increase in training speed and exhibits much lower test time compared to traditional Riemannian methods. Based on these results, we believe that the proposed MLCSP-TSE-MLP is a powerful tool for handling high-dimensional data and holds great potential for practical applications.

Details DOI

NeurIPS Conference 2024 Conference Paper

Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?

Pedro R. Bassi
Wenxuan Li
Yucheng Tang
Fabian Isensee
Zifu Wang
Jieneng Chen
Yu-Cheng Chou
Saikat Roy

How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks does not guarantee success in real-world scenarios. To address these problems, we present Touchstone, a large-scale collaborative segmentation benchmark of 9 types of abdominal organs. This benchmark is based on 5, 195 training CT scans from 76 hospitals around the world and 5, 903 testing CT scans from 11 additional hospitals. This diverse test set enhances the statistical significance of benchmark results and rigorously evaluates AI algorithms across various out-of-distribution scenarios. We invited 14 inventors of 19 AI algorithms to train their algorithms, while our team, as a third party, independently evaluated these algorithms on three test sets. In addition, we also evaluated pre-existing AI frameworks---which, differing from algorithms, are more flexible and can support different algorithms—including MONAI from NVIDIA, nnU-Net from DKFZ, and numerous other open-source frameworks. We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain.

PDF Details DOI

EAAI Journal 2022 Journal Article

Secure distributed estimation under Byzantine attack and manipulation attack

Fangyi Wan
Ting Ma
Yi Hua
Bin Liao
Xinlin Qing

Wireless sensor networks (WSN) with distributed cooperation has been widely used in various fields due to their strong adaptive learning ability. However, WSN is vulnerable to malicious attacks, and the damaging behaviors of these attacks would make sensor nodes work unsatisfactorily and then contaminate the entire network. Although some security algorithms have been proposed to detect these malicious attacks, such as manipulation attack and Byzantine attack, they are not robust enough. To ameliorate this situation, a secure distributed diffusion least-mean-square (LMS) algorithm is designed, which adopts the dual detection mechanisms over the designed two subsystems. One subsystem is based on the LMS with cooperative strategy (L-CS), which uses an angle detector to filter manipulation attack, while the other subsystem is based on the LMS with non-cooperative strategy (L-NCS), where the sensors utilize non-cooperation to improve the detection effect on Byzantine attack. Moreover, the L-CS subsystem could further provide the secure estimation for the proposed algorithm by isolating malicious nodes. The performances are analyzed from the mean and mean-square convergence. Finally, some simulations are implemented to prove the effectiveness of the proposed algorithm.

Details DOI

YNICL Journal 2021 Journal Article

Alteration of brain structural connectivity in progression of Parkinson's disease: A connectome-wide network analysis

Yanwu Yang
Chenfei Ye
Junyan Sun
Li Liang
Haiyan Lv
Linlin Gao
Jiliang Fang
Ting Ma

Pinpointing the brain dysconnectivity in idiopathic rapid eye movement sleep behaviour disorder (iRBD) can facilitate preventing the conversion of Parkinson's disease (PD) from prodromal phase. Recent neuroimage investigations reported disruptive brain white matter connectivity in both iRBD and PD, respectively. However, the intrinsic process of the human brain structural network evolving from iRBD to PD still remains largely unknown. To address this issue, 151 participants including iRBD, PD and age-matched normal controls were recruited to receive diffusion MRI scans and neuropsychological examinations. The connectome-wide association analysis was performed to detect reorganization of brain structural network along with PD progression. Eight brain seed regions in both cortical and subcortical areas demonstrated significant structural pattern changes along with the progression of PD. Applying machine learning on the key connectivity related to these seed regions demonstrated better classification accuracy compared to conventional network-based statistic. Our study shows that connectome-wide association analysis reveals the underlying structural connectivity patterns related to the progression of PD, and provide a promising distinct capability to predict prodromal PD patients.

Details DOI

YNICL Journal 2019 Journal Article

Connectome-wide network analysis of white matter connectivity in Alzheimer's disease

Chenfei Ye
Susumu Mori
Piu Chan
Ting Ma

A multivariate analytical strategy may pinpoint the structural connectivity patterns associated with Alzheimer's disease (AD) pathology in connectome-wide association studies. Diffusion magnetic resonance imaging data from 161 participants including subjects with healthy controls, AD, stable and converting mild cognitive impairment, were selected for group-wise comparisons. A multivariate distance matrix regression (MDMR) analysis was performed to detect abnormality in brain structural network along with disease progression. Based on the seed regions returned by the MDMR analysis, supervised learning was applied to evaluate the disease predictive performance. Nine brain regions, including the left orbital part of superior and middle frontal gyrus, the bilateral supplementary motor area, the bilateral insula, the left hippocampus, the left putamen, and the left thalamus demonstrated extremely significant structural pattern changes along with the progression of AD. The disease classification was more efficient when based on the key connectivity related to these seed regions than when based on whole-brain structural connectivity. MDMR analysis reveals brain network reorganization caused by AD pathology. The key structural connectivity detected in this study exhibits promising distinguishing capability to predict prodromal AD patients.

Details DOI

YNICL Journal 2019 Journal Article

Multi-atlas based detection and localization (MADL) for location-dependent quantification of white matter hyperintensities

Dan Wu
Marilyn Albert
Anja Soldan
Corinne Pettigrew
Kenichi Oishi
Yusuke Tomogane
Chenfei Ye
Ting Ma

The extent and spatial location of white matter hyperintensities (WMH) on brain MRI may be relevant to the development of cognitive decline in older persons. Here, we introduce a new method, known as the Multi-atlas based Detection and Localization (MADL), to evaluate WMH on fluid-attenuated inversion recovery (FLAIR) data. This method simultaneously parcellates the whole brain into 143 structures and labels hyperintense areas within each WM structure. First, a multi-atlas library was established with FLAIR data of normal elderly brains; and then a multi-atlas fusion algorithm was developed by which voxels with locally abnormal intensities were detected as WMH. At the same time, brain segmentation maps were generated from the multi-atlas fusion process to determine the anatomical location of WMH. Areas identified using the MADL method agreed well with manual delineation, with an interclass correlation of 0.97 and similarity index (SI) between 0.55 and 0.72, depending on the total WMH load. Performance was compared to other state-of-the-art WMH detection methods, such as BIANCA and LST. MADL-based analyses of WMH in an older population revealed a significant association between age and WMH load in deep WM but not subcortical WM. The findings also suggested increased WMH load in selective brain regions in subjects with mild cognitive impairment compared to controls, including the inferior deep WM and occipital subcortical WM. The proposed MADL approach may facilitate location-dependent characterization of WMH in older individuals with memory impairment.

Details DOI

YNIMG Journal 2016 Journal Article

Resource atlases for multi-atlas brain segmentations with multiple ontology levels based on T1-weighted MRI

Dan Wu
Ting Ma
Can Ceritoglu
Yue Li
Jill Chotiyanonta
Zhipeng Hou
John Hsu
Xin Xu

Technologies for multi-atlas brain segmentation of T1-weighted MRI images have rapidly progressed in recent years, with highly promising results. This approach, however, relies on a large number of atlases with accurate and consistent structural identifications. Here, we introduce our atlas inventories (n=90), which cover ages 4–82years with unique hierarchical structural definitions (286 structures at the finest level). This multi-atlas library resource provides the flexibility to choose appropriate atlases for various studies with different age ranges and structure-definition criteria. In this paper, we describe the details of the atlas resources and demonstrate the improved accuracy achievable with a dynamic age-matching approach, in which atlases that most closely match the subject's age are dynamically selected. The advanced atlas creation strategy, together with atlas pre-selection principles, is expected to support the further development of multi-atlas image segmentation.

Details DOI