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Rongpin Wang

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8 papers

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EAAI Journal 2026 Journal Article

Integrating whole-slide images and transcriptomic data for survival analysis using multimodal attention networks

Chunfeng Shao
Yuanshen Zhao
Yinsheng Chen
Jingxian Duan
Zeyu Zhang
Rongpin Wang
Dong Liang
Dongyue Chen

The integration of multimodal data holds great promise for tumor prognosis prediction by providing a more comprehensive view of the tumor. However, current fusion algorithms, which either merge feature levels or use alignment mechanisms for complex integration, often overlook the relationships between different modalities. To address this issue, we propose a novel multimodal deep learning algorithm that integrates pathological images and transcriptomic data for tumor prognosis prediction. Our model not only uses information from each data modality but also mines the correlations between the two modalities through a Hierarchical Cross-Modal Attention (HCMA) mechanism. In the pathological branch, we designed a self-attention encoder and decoder to extract various levels of contextual features from the pathological images. In the transcriptomic branch, we employed a selective state space module specifically designed to capture the complex dependencies among genetic features. To capture the correlations between the two modalities, we used an HCMA transformer module to generate cross-modal features, including gene-guided pathological features and pathology-guided gene features. Finally, we apply a specific feature alignment mechanism to constrain these features before fusing them to predict survival outcomes. We validated the proposed algorithm on four diverse cancer datasets from The Cancer Genome Atlas (TCGA). For testing the performance of the proposed survival prediction model, we also validated it on an independent cohort of 86 glioma patients from Sun Yat-sen University Cancer Center. Results show the model performs excellently in cancer survival analysis, which outperforms comparative single-modal models and multimodal fusion models.

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YNIMG Journal 2026 Journal Article

Multimodal radiomics of precisely segmented hippocampal subfields: Iron deposition and structural biomarkers for early diagnosis of Alzheimer's disease

Dongxue Li
Junjie He
Benqin Liu
Lin Zhu
Yuezong Yang
Yunsong Peng
Lisha Nie
Rongpin Wang

Profiling imaging biomarkers of prodromal Alzheimer's disease (AD) against AD dementia may aid earlier diagnosis, yet approaches jointly capturing iron-related pathology and hippocampal subfield heterogeneity remain scarce. We developed a hippocampal-subfield multimodal radiomics framework integrating quantitative susceptibility mapping (QSM) and 3D T1-weighted MRI. A primary cohort of 92 participants (50 prodromal AD, 42 CE dementia) and an independent external cohort of 30 (15/15) were included. Twenty-four hippocampal subfields were segmented on super-resolution T1 images and propagated to co-registered QSM for feature extraction. Radiomic features were condensed into a radiomics score (Rad-score) via a training-only selection pipeline. Using the Rad-score as the sole predictor, a support vector machine (SVM) classifier was trained. On the external cohort, the SVM achieved an area under the receiver operating characteristic curve of 0.85 and an accuracy of 0.83. The predictive signature was dominated by QSM texture features in Cornu Ammonis 1 and the granule cell layer of the dentate gyrus, complemented by T1 first-order heterogeneity. Modality ablation suggested potential-but not definitive-complementarity of multimodal integration. This framework shows promise for AD stage classification and warrants further validation in larger independent cohorts.

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YNIMG Journal 2026 Journal Article

PI-uMSS: Prior information-based unsupervised magnetic source separation in quantitative susceptibility mapping

Junjie He
Bangkang Fu
Cen Pan
Lisha Nie
Rui Xu
Zi Xu
Rongpin Wang

Magnetic source separation (MSS) in quantitative susceptibility mapping (QSM) provides a powerful tool to disentangle paramagnetic and diamagnetic contributions, enabling more accurate quantification of brain iron and myelin alterations. However, existing MSS approaches typically depend on approximations derived from reversible transverse relaxation (R2'=R2∗-R2) or extrapolate from a limited number of brain regions to perform whole-brain separation. Furthermore, current deep learning-based methods often require extensive and high-quality labels, which are difficult to obtain. To address these limitations, we propose an unsupervised MSS framework guided by prior information and constrained by physics-informed loss functions to improve separation fidelity. The proposed model directly processes whole-brain QSM and R2∗ data, infers intermediate parameters, and reconstructs the spatial distributions of paramagnetic and diamagnetic sources via biophysical modeling. Experimental results show that the method achieves high structural similarity (SSIM = 0.9945 for paramagnetic and 0.9942 for diamagnetic components) and a low normalized mean square error (0.11) relative to the original QSM, demonstrating robust and consistent source decomposition performance. Code is available at https://github.com/TyrionJ/PI-uMSS.

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JBHI Journal 2025 Journal Article

3D Isotropic High-Resolution Fetal Brain MRI Reconstruction From Motion Corrupted Thick Data Based on Physical-Informed Unsupervised Learning

Jiangjie Wu
Lixuan Chen
Zhenghao Li
Xin Li
Taotao Sun
Lihui Wang
Rongpin Wang
Hongjiang Wei

High-quality 3D fetal brain MRI reconstruction from motion-corrupted 2D slices is crucial for precise clinical diagnosis and advancing our understanding of fetal brain development. This necessitates reliable slice-to-volume registration (SVR) for motion correction and super-resolution reconstruction (SRR) techniques. Traditional approaches have their limitations, but deep learning (DL) offers the potential in enhancing SVR and SRR. However, most of DL methods require large-scale external 3D high-resolution (HR) training datasets, which is challenging in clinical fetal MRI. To address this issue, we propose an unsupervised iterative joint SVR and SRR DL framework for 3D isotropic HR volume reconstruction. Specifically, our method conceptualizes SVR as a function that maps a 2D slice and a 3D target volume to a rigid transformation matrix, aligning the slice to the underlying location within the target volume. This function is parameterized by a convolutional neural network, which is trained by minimizing the difference between the volume slicing at the predicted position and the actual input slice. For SRR, a decoding network embedded within a deep image prior framework, coupled with a comprehensive image degradation model, is used to produce the HR volume. The deep image prior framework offers a local consistency prior to guide the reconstruction of HR volumes. By performing a forward degradation model, the HR volume is optimized by minimizing the loss between the predicted slices and the acquired slices. Experiments on both large-magnitude motion-corrupted simulation data and clinical data have shown that our proposed method outperforms current state-of-the-art fetal brain reconstruction methods.

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YNIMG Journal 2023 Journal Article

msQSM: Morphology-based self-supervised deep learning for quantitative susceptibility mapping

Junjie He
Yunsong Peng
Bangkang Fu
Yuemin Zhu
Lihui Wang
Rongpin Wang

Quantitative susceptibility mapping (QSM) has been applied to the measurement of iron deposition and the auxiliary diagnosis of neurodegenerative disease. There still exists a dipole inversion problem in QSM reconstruction. Recently, deep learning approaches have been proposed to resolve this problem. However, most of these approaches are supervised methods that need pairs of the input phase and ground-truth. It remains a challenge to train a model for all resolutions without using the ground-truth and only using one resolution data. To address this, we proposed a self-supervised QSM deep learning method based on morphology. It consists of a morphological QSM builder to decouple the dependency of the QSM on acquisition resolution, and a morphological loss to reduce artifacts effectively and save training time efficiently. The proposed method can reconstruct arbitrary resolution QSM on both human data and animal data, regardless of whether the resolution is higher or lower than that of the training set. Our method outperforms the previous best unsupervised method with a 3.6% higher peak signal-to-noise ratio, 16.2% lower normalized root mean square error, and 22.1% lower high-frequency error norm. The morphological loss reduces training time by 22.1% with respect to the cycle gradient loss used in the previous unsupervised methods. Experimental results show that the proposed method accurately measures QSM with arbitrary resolutions, and achieves state-of-the-art results among unsupervised deep learning methods. Research on applications in neurodegenerative diseases found that our method is robust enough to measure significant increase in striatal magnetic susceptibility in patients during Alzheimer's disease progression, as well as significant increase in substantia nigra susceptibility in Parkinson's disease patients, and can be used as an auxiliary differential diagnosis tool for Alzheimer's disease and Parkinson's disease.

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JBHI Journal 2021 Journal Article

2D and 3D CT Radiomic Features Performance Comparison in Characterization of Gastric Cancer: A Multi-Center Study

Lingwei Meng
Di Dong
Xin Chen
Mengjie Fang
Rongpin Wang
Jing Li
Zaiyi Liu
Jie Tian

Objective: Radiomics, an emerging tool for medical image analysis, is potential towards precisely characterizing gastric cancer (GC). Whether using one-slice 2D annotation or whole-volume 3D annotation remains a long-time debate, especially for heterogeneous GC. We comprehensively compared 2D and 3D radiomic features’ representation and discrimination capacity regarding GC, via three tasks ( ${\boldsymbol{T}^{\boldsymbol{LNM}}}$, lymph node metastasis’ prediction; ${\boldsymbol{T}^{\boldsymbol{LVI}}}$, lymphovascular invasion's prediction; ${\boldsymbol{T}^{\boldsymbol{pT}}}$, pT4 or other pT stages’ classification). Methods: Four-center 539 GC patients were retrospectively enrolled and divided into the training and validation cohorts. From 2D or 3D regions of interest (ROIs) annotated by radiologists, radiomic features were extracted respectively. Feature selection and model construction procedures were customed for each combination of two modalities (2D or 3D) and three tasks. Subsequently, six machine learning models ( $\boldsymbol{Model}_{2\boldsymbol{D}}^{\boldsymbol{LNM}}$, $\boldsymbol{Model}_{3\boldsymbol{D}}^{\boldsymbol{LNM}}$; $\boldsymbol{Model}_{2\boldsymbol{D}}^{\boldsymbol{LVI}}$, $\boldsymbol{Model}_{3\boldsymbol{D}}^{\boldsymbol{LVI}}$; $\boldsymbol{Model}_{2\boldsymbol{D}}^{\boldsymbol{pT}}$, $\boldsymbol{Model}_{3\boldsymbol{D}}^{\boldsymbol{pT}}$ ) were derived and evaluated to reflect modalities’ performances in characterizing GC. Furthermore, we performed an auxiliary experiment to assess modalities’ performances when resampling spacing different. Results: Regarding three tasks, the yielded areas under the curve (AUCs) were: $\boldsymbol{Model}_{2\boldsymbol{D}}^{\boldsymbol{LNM}}$ 's 0. 712 (95% confidence interval, 0. 613–0. 811), $\boldsymbol{Model}_{3\boldsymbol{D}}^{\boldsymbol{LNM}}$ 's 0. 680 (0. 584–0. 775); $\boldsymbol{Model}_{2\boldsymbol{D}}^{\boldsymbol{LVI}}$ 's 0. 677 (0. 595–0. 761), $\boldsymbol{Model}_{3\boldsymbol{D}}^{\boldsymbol{LVI}}$ 's 0. 615 (0. 528-0. 703); $\boldsymbol{Model}_{2\boldsymbol{D}}^{\boldsymbol{pT}}$ 's 0. 840 (0. 779–0. 901), $\boldsymbol{Model}_{3\boldsymbol{D}}^{\boldsymbol{pT}}$ 's 0. 813 (0. 747–0. 879). Moreover, the auxiliary experiment indicated that $\boldsymbol{Model}{\boldsymbol{s}_{2\boldsymbol{D}}}$ are statistically advantageous than $\boldsymbol{Model}{\boldsymbol{s}_{3\boldsymbol{D}}}$ with different resampling spacings. Conclusion: Models constructed with 2D radiomic features revealed comparable performances with those constructed with 3D features in characterizing GC. Significance: Our work indicated that time-saving 2D annotation would be the better choice in GC, and provided a related reference to further radiomics-based researches.

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JBHI Journal 2021 Journal Article

Learning a Deep CNN Denoising Approach Using Anatomical Prior Information Implemented With Attention Mechanism for Low-Dose CT Imaging on Clinical Patient Data From Multiple Anatomical Sites

Zhenxing Huang
Xinfeng Liu
Rongpin Wang
Zixiang Chen
Yongfeng Yang
Xin Liu
Hairong Zheng
Dong Liang

Dose reduction in computed tomography (CT) has gained considerable attention in clinical applications because it decreases radiation risks. However, a lower dose generates noise in low-dose computed tomography (LDCT) images. Previous deep learning (DL)-based works have investigated ways to improve diagnostic performance to address this ill-posed problem. However, most of them disregard the anatomical differences among different human body sites in constructing the mapping function between LDCT images and their high-resolution normal-dose CT (NDCT) counterparts. In this article, we propose a novel deep convolutional neural network (CNN) denoising approach by introducing information of the anatomical prior. Instead of designing multiple networks for each independent human body anatomical site, a unified network framework is employed to process anatomical information. The anatomical prior is represented as a pattern of weights of the features extracted from the corresponding LDCT image in an anatomical prior fusion module. To promote diversity in the contextual information, a spatial attention fusion mechanism is introduced to capture many local regions of interest in the attention fusion module. Although many network parameters are saved, the experimental results demonstrate that our method, which incorporates anatomical prior information, is effective in denoising LDCT images. Furthermore, the anatomical prior fusion module could be conveniently integrated into other DL-based methods and avails the performance improvement on multiple anatomical data.

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JBHI Journal 2021 Journal Article

Multi-Focus Network to Decode Imaging Phenotype for Overall Survival Prediction of Gastric Cancer Patients

Liwen Zhang
Di Dong
Lianzhen Zhong
Cong Li
Chaoen Hu
Xin Yang
Zaiyi Liu
Rongpin Wang

Gastric cancer (GC) is the third leading cause of cancer-associated deaths globally. Accurate risk prediction of the overall survival (OS) for GC patients shows significant prognostic value, which helps identify and classify patients into different risk groups to benefit from personalized treatment. Many methods based on machine learning algorithms have been widely explored to predict the risk of OS. However, the accuracy of risk prediction has been limited and remains a challenge with existing methods. Few studies have proposed a framework and pay attention to the low-level and high-level features separately for the risk prediction of OS based on computed tomography images of GC patients. To achieve high accuracy, we propose a multi-focus fusion convolutional neural network. The network focuses on low-level and high-level features, where a subnet to focus on lower-level features and the other enhanced subnet with lateral connection to focus on higher-level semantic features. Three independent datasets of 640 GC patients are used to assess our method. Our proposed network is evaluated by metrics of the concordance index and hazard ratio. Our network outperforms state-of-the-art methods with the highest concordance index and hazard ratio in independent validation and test sets. Our results prove that our architecture can unify the separate low-level and high-level features into a single framework, and can be a powerful method for accurate risk prediction of OS.

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