Jinxi Xiang

All Publications

• A unified vision-language model for precision oncology and biomarker prediction in neuroblastoma. Nature communications Zhu, J., Hu, R., Yang, S., Sun, Q., Zhao, Z., Cao, J., Pan, P., Wang, K., Cui, L., Tang, H., Fang, Q., Jiang, S., Lei, L., Zhang, W., Xie, J., Kang, S., Xiao, D., Xiao, M., Zhai, X., Jia, Y., Chen, J., Yuan, W., Han, X., Lu, L., Zhao, J., Wang, X., Li, Y., Xiang, J., Zhu, B. 2026

  Abstract

  Neuroblastoma is a leading cause of childhood cancer mortality, presenting persistent management challenges due to its biological heterogeneity and the limited accessibility of molecular profiling in routine practice. Here we present NEVA (NEuroblastoma Vision-language AI), a multimodal foundation model designed to address these barriers. Unlike conventional approaches that rely on frozen encoders and multiple instance learning, NEVA implements a pathologist-inspired hierarchical workflow with end-to-end optimization. Developed and evaluated in a large multi-institutional cohort of 1,238 patients across multiple centers, NEVA outperforms ten representative foundation models, including TITAN, UNI, and Virchow, across the majority of the 11 clinical tasks evaluated. The model demonstrates diagnostic capability, achieving Area Under the Receiver Operating Characteristic curves of 0.916 for subtype classification, 0.823 for Shimada classification, and 0.806 for risk group stratification. Furthermore, NEVA predicts key molecular alterations from routinely available pathology data, reaching an AUROC of 0.924 for NMYC amplification and 0.830 for 1p36 deletion, while enabling prognostic stratification for progression-free and overall survival across multiple test cohorts. By integrating interpretable attention maps that localize histologically relevant regions, NEVA establishes a scalable framework for neuroblastoma risk stratification and clinical decision support.

  View details for DOI 10.1038/s41467-026-74865-5

  View details for PubMedID 42426002

• Cellular architecture and neighborhood-informed virtual spatial tumor profiling from histopathology. Cell Li, Y., Li, Z., Quinton, R., Ji, Y., Zhang, X., Xiang, J., Wang, X., Yang, S., Eweje, F., Chen, Y., Luo, X., Li, Y., Mulholland, J., Chen, S., Bergstrom, C., Kim, T., Maria Olguin, F., Willens, S., Lin, S. H., Nirschl, J. J., West, R., Neal, J., Diehn, M., Li, R. 2026

  Abstract

  The tumor microenvironment (TME) critically shapes disease progression and therapeutic resistance. However, a comprehensive understanding of its spatial architecture remains elusive, and clinical translation is challenging. Here, we present cellular architecture and neighborhood-informed virtual AI-driven spatial profiling (CANVAS), an artificial intelligence platform that infers tumor ecological habitats from hematoxylin and eosin (H&E) histopathology. Built on an atlas of over 18 million cells profiled by 41-plex spatial proteomics across 457 patients with non-small cell lung cancer, CANVAS establishes 10 reproducible cellular neighborhoods (CNs) capturing conserved spatial organization of the TME. Through multimodal alignment and foundation-model-based morphological encoding, CANVAS predicts CN-anchored habitat structures from H&E slides and enables clinical evaluation in over 5,000 patients spanning 9 cancer types. Across patient cohorts, CANVAS supports prognostic modeling, spatial ecotype stratification, and immunotherapy outcome prediction. These results establish CANVAS as a clinically scalable platform for spatial profiling, bridging single-cell analysis to population-level insight and enabling precision oncology.

  View details for DOI 10.1016/j.cell.2026.05.031

  View details for PubMedID 42302781

• Toward General-Purpose Video Reconstruction Through Synergy of Grid-Splicing Diffusion and Large Language Models IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY Liu, J., Zhang, J., Yang, S., Xiang, J., Wang, X., Zhao, J., Yang, Z., Zhao, J. 2026; 36 (2): 1328-1340

  View details for DOI 10.1109/TCSVT.2025.3545795

  View details for Web of Science ID 001687411500023

• AI-enabled virtual spatial proteomics from histopathology for interpretable biomarker discovery in lung cancer. Nature medicine Li, Z., Li, Y., Xiang, J., Wang, X., Yang, S., Zhang, X., Eweje, F., Chen, Y., Luo, X., Li, Y., Mulholland, J., Bergstrom, C., Kim, T., Olguin, F. M., Willens, S., Nirschl, J. J., West, R., Neal, J., Diehn, M., Li, R. 2026

  Abstract

  Spatial proteomics enables high-resolution mapping of protein expression and can transform our understanding of biology and disease. However, major challenges remain for clinical translation, including cost, complexity and scalability. Here we present H&E to protein expression (HEX), an AI model designed to computationally generate spatial proteomics profiles from standard histopathology slides. Trained and validated on 819,000 histopathology image tiles with matched protein expression from 382 tumor samples, HEX accurately predicts the expression of 40 biomarkers encompassing immune, structural and functional programs. HEX demonstrates substantial performance gains over alternative methods for protein expression prediction from H&E images. We develop a multimodal data integration approach that combines the original H&E image and AI-derived virtual spatial proteomics to enhance outcome prediction. Applied to six independent non-small-cell lung cancer cohorts totaling 2,298 patients, HEX-enabled multimodal integration improved prognostic accuracy by 22% and immunotherapy response prediction by 24-39% compared with conventional clinicopathological and molecular biomarkers. Biological interpretation revealed spatially organized tumor-immune niches predictive of therapeutic response, including the co-localization of T helper cells and cytotoxic T cells in responders, and immunosuppressive tumor-associated macrophage and neutrophil aggregates in non-responders. HEX provides a low-cost and scalable approach to study spatial biology and enables the discovery and clinical translation of interpretable biomarkers for precision medicine.

  View details for DOI 10.1038/s41591-025-04060-4

  View details for PubMedID 41491099

• Pancancer outcome prediction via a unified weakly supervised deep learning model. Signal transduction and targeted therapy Yuan, W., Chen, Y., Zhu, B., Yang, S., Zhang, J., Mao, N., Xiang, J., Li, Y., Ji, Y., Luo, X., Zhang, K., Xing, X., Kang, S., Xiao, D., Wang, F., Wu, J., Zhang, H., Tang, H., Maurya, H., Corredor, G., Barrera, C., Zhou, Y., Pandav, K., Zhao, J., Jain, P., Delasos, L., Huang, J., Yang, K., Teknos, T. N., Lewis, J., Koyfman, S., Pennell, N. A., Yu, K. H., Han, X., Zhang, J., Wang, X., Madabhushi, A. 2025; 10 (1): 285

  Abstract

  Accurate prognosis prediction is essential for guiding cancer treatment and improving patient outcomes. While recent studies have demonstrated the potential of histopathological images in survival analysis, existing models are typically developed in a cancer-specific manner, lack extensive external validation, and often rely on molecular data that are not routinely available in clinical practice. To address these limitations, we present PROGPATH, a unified model capable of integrating histopathological image features with routinely collected clinical variables to achieve pancancer prognosis prediction. PROGPATH employs a weakly supervised deep learning architecture built upon the foundation model for image encoding. Morphological features are aggregated through an attention-guided multiple instance learning module and fused with clinical information via a cross-attention transformer. A router-based classification strategy further refines the prediction performance. PROGPATH was trained on 7999 whole-slide images (WSIs) from 6,670 patients across 15 cancer types, and extensively validated on 17 external cohorts with a total of 7374 WSIs from 4441 patients, covering 12 cancer types from 8 consortia and institutions across three continents. PROGPATH achieved consistently superior performance compared with state-of-the-art multimodal prognosis prediction models. It demonstrated strong generalizability across cancer types and robustness in stratified subgroups, including early- and advanced-stage patients, treatment cohorts (radiotherapy and pharmaceutical therapy), and biomarker-defined subsets. We further provide model interpretability by identifying pathological patterns critical to PROGPATH's risk predictions, such as the degree of cell differentiation and extent of necrosis. Together, these results highlight the potential of PROGPATH to support pancancer outcome prediction and inform personalized cancer management strategies.

  View details for DOI 10.1038/s41392-025-02374-w

  View details for PubMedID 40897689

  View details for PubMedCentralID PMC12405520

• Foundation Model for Predicting Prognosis and Adjuvant Therapy Benefit From Digital Pathology in GI Cancers. Journal of clinical oncology : official journal of the American Society of Clinical Oncology Wang, X., Jiang, Y., Yang, S., Wang, F., Zhang, X., Wang, W., Chen, Y., Wu, X., Xiang, J., Li, Y., Jiang, X., Yuan, W., Zhang, J., Yu, K. H., Ward, R. L., Hawkins, N., Jonnagaddala, J., Li, G., Li, R. 2025: JCO2401501

  Abstract

  Artificial intelligence (AI) holds significant promise for improving cancer diagnosis and treatment. Here, we present a foundation AI model for prognosis prediction on the basis of standard hematoxylin and eosin-stained histopathology slides.In this multinational cohort study, we developed AI models to predict prognosis from histopathology images of patients with GI cancers. First, we trained a foundation model using over 130 million patches from 104,876 whole-slide images on the basis of self-supervised learning. Second, we fine-tuned deep learning models for predicting survival outcomes and validated them across seven cohorts, including 1,619 patients with gastric and esophageal cancers and 2,594 patients with colorectal cancer. We further assessed the model for predicting survival benefit from adjuvant chemotherapy.The AI models predicted disease-free survival and disease-specific survival with a concordance index of 0.726-0.797 for gastric cancer and 0.714-0.757 for colorectal cancer in the validation cohorts. The models stratified patients into high-risk and low-risk groups, with 5-year survival rates of 49%-52% versus 76%-92% in gastric cancer and 43%-72% versus 81%-98% in colorectal cancer. In multivariable analysis, the AI risk scores remained an independent prognostic factor after adjusting for clinicopathologic variables. Compared with stage alone, an integrated model consisting of stage and image information improved prognosis prediction across all validation cohorts. Finally, adjuvant chemotherapy was associated with improved survival in the high-risk group but not in the low-risk group (treatment-model interaction P = .01 and .006) for stage II/III gastric and colorectal cancer, respectively.The pathology foundation model can accurately predict survival outcomes and complement clinicopathologic factors in GI cancers. Pending prospective validation, it may be used to improve risk stratification and inform personalized adjuvant therapy.

  View details for DOI 10.1200/JCO-24-01501

  View details for PubMedID 40168636

• Deep Learning-Enabled Integration of Histology and Transcriptomics for Tissue Spatial Profile Analysis. Research (Washington, D.C.) Ge, Y., Leng, J., Tang, Z., Wang, K., U, K., Zhang, S. M., Han, S., Zhang, Y., Xiang, J., Yang, S., Liu, X., Song, Y., Wang, X., Li, Y., Zhao, J. 2025; 8: 0568

  Abstract

  Spatially resolved transcriptomics enable comprehensive measurement of gene expression at subcellular resolution while preserving the spatial context of the tissue microenvironment. While deep learning has shown promise in analyzing SCST datasets, most efforts have focused on sequence data and spatial localization, with limited emphasis on leveraging rich histopathological insights from staining images. We introduce GIST, a deep learning-enabled gene expression and histology integration for spatial cellular profiling. GIST employs histopathology foundation models pretrained on millions of histology images to enhance feature extraction and a hybrid graph transformer model to integrate them with transcriptome features. Validated with datasets from human lung, breast, and colorectal cancers, GIST effectively reveals spatial domains and substantially improves the accuracy of segmenting the microenvironment after denoising transcriptomics data. This enhancement enables more accurate gene expression analysis and aids in identifying prognostic marker genes, outperforming state-of-the-art deep learning methods with a total improvement of up to 49.72%. GIST provides a generalizable framework for integrating histology with spatial transcriptome analysis, revealing novel insights into spatial organization and functional dynamics.

  View details for DOI 10.34133/research.0568

  View details for PubMedID 39830364

  View details for PubMedCentralID PMC11739434

• A vision-language foundation model for precision oncology. Nature Xiang, J., Wang, X., Zhang, X., Xi, Y., Eweje, F., Chen, Y., Li, Y., Bergstrom, C., Gopaulchan, M., Kim, T., Yu, K. H., Willens, S., Olguin, F. M., Nirschl, J. J., Neal, J., Diehn, M., Yang, S., Li, R. 2025

  Abstract

  Clinical decision-making is driven by multimodal data, including clinical notes and pathological characteristics. Artificial intelligence approaches that can effectively integrate multimodal data hold significant promise in advancing clinical care1,2. However, the scarcity of well-annotated multimodal datasets in clinical settings has hindered the development of useful models. In this study, we developed the Multimodal transformer with Unified maSKed modeling (MUSK), a vision-language foundation model designed to leverage large-scale, unlabelled, unpaired image and text data. MUSK was pretrained on 50 million pathology images from 11,577 patients and one billion pathology-related text tokens using unified masked modelling. It was further pretrained on one million pathology image-text pairs to efficiently align the vision and language features. With minimal or no further training, MUSK was tested in a wide range of applications and demonstrated superior performance across 23 patch-level and slide-level benchmarks, including image-to-text and text-to-image retrieval, visual question answering, image classification and molecular biomarker prediction. Furthermore, MUSK showed strong performance in outcome prediction, including melanoma relapse prediction, pan-cancer prognosis prediction and immunotherapy response prediction in lung and gastro-oesophageal cancers. MUSK effectively combined complementary information from pathology images and clinical reports and could potentially improve diagnosis and precision in cancer therapy.

  View details for DOI 10.1038/s41586-024-08378-w

  View details for PubMedID 39779851

  View details for PubMedCentralID 9586871

• Deep learning-based diagnosis and survival prediction of patients with renal cell carcinoma from primary whole slide images. Pathology Chen, S., Wang, X., Zhang, J., Jiang, L., Gao, F., Xiang, J., Yang, S., Yang, W., Zheng, J., Han, X. 2024

  Abstract

  There is an urgent clinical demand to explore novel diagnostic and prognostic biomarkers for renal cell carcinoma (RCC). We proposed deep learning-based artificial intelligence strategies. The study included 1752 whole slide images from multiple centres. Based on the pixel-level of RCC segmentation, the diagnosis diagnostic model achieved an area under the receiver operating characteristic curve (AUC) of 0.977 (95% CI 0.969-0.984) in the external validation cohort. In addition, our diagnostic model exhibited excellent performance in the differential diagnosis of RCC from renal oncocytoma, which achieved an AUC of 0.951 (0.922-0.972). The graderisk for the recognition of high-grade tumour achieved AUCs of 0.840 (0.805-0.871) in the Cancer Genome Atlas (TCGA) cohort, 0.857 (0.813-0.894) in the Shanghai General Hospital (General) cohort, and 0.894 (0.842-0.933) in the Clinical Proteomic Tumor Analysis Consortium (CPTAC) cohort, for the recognition of high-grade tumour. The OSrisk for predicting 5-year survival status achieved an AUC of 0.784 (0.746-0.819) in the TCGA cohort, which was further verified in the independent general cohort and the CPTAC cohort, with AUCs of 0.774 (0.723-0.820) and 0.702 (0.632-0.765), respectively. Moreover, the competing-risk nomogram (CRN) showed its potential to be a prognostic indicator, with a hazard ratio (HR) of 5.664 (3.893-8.239, p<0.0001), outperforming other traditional clinical prognostic indicators. Kaplan-Meier survival analysis further illustrated that our CRN could significantly distinguish patients with high survival risk. Deep learning-based artificial intelligence could be a useful tool for clinicians to diagnose and predict the prognosis of RCC patients, thus improving the process of individualised treatment.

  View details for DOI 10.1016/j.pathol.2024.05.012

  View details for PubMedID 39168777

• Deep learning-based pathology signature could reveal lymph node status and act as a novel prognostic marker across multiple cancer types BRITISH JOURNAL OF CANCER Chen, S., Xiang, J., Wang, X., Zhang, J., Yang, S., Yang, W., Zheng, J., Han, X. 2023; 129 (1): 46-53

  Abstract

  Identifying lymph node metastasis (LNM) relies mainly on indirect radiology. Current studies omitted the quantified associations with traits beyond cancer types, failing to provide generalisation performance across various tumour types.4400 whole slide images across 11 cancer types were collected for training, cross-verification, and external validation of the pan-cancer lymph node metastasis (PC-LNM) model. We proposed an attention-based weakly supervised neural network based on self-supervised cancer-invariant features for the prediction task.PC-LNM achieved a test area under the curve (AUC) of 0.732 (95% confidence interval: 0.717-0.746, P < 0.0001) in fivefold cross-validation of multiple cancer types, which also demonstrated good generalisation in the external validation cohort with AUC of 0.699 (95% confidence interval: 0.658-0.737, P < 0.0001). The interpretability results derived from PC-LNM revealed that the regions with the highest attention scores identified by the model generally correspond to tumours with poorly differentiated morphologies. PC-LNM achieved superior performance over previously reported methods and could also act as an independent prognostic factor for patients across multiple tumour types.We presented an automated pan-cancer model for predicting the LNM status from primary tumour histology, which could act as a novel prognostic marker across multiple cancer types.

  View details for DOI 10.1038/s41416-023-02262-6

  View details for Web of Science ID 000980794300002

  View details for PubMedID 37137998

  View details for PubMedCentralID PMC10307798

• The devil is in the details: a small-lesion sensitive weakly supervised learning framework for prostate cancer detection and grading VIRCHOWS ARCHIV Yang, Z., Wang, X., Xiang, J., Zhang, J., Yang, S., Wang, X., Yang, W., Li, Z., Han, X., Liu, Y. 2023; 482 (3): 525-538

  Abstract

  Prostate cancer (PCa) is a significant health concern in aging males, and the diagnosis depends primarily on histopathological assessments to determine tumor size and Gleason score. This process is highly time-consuming, subjective, and relies on the extensive experience of the pathologists. Deep learning based artificial intelligence shows an ability to match pathologists on many prostate cancer diagnostic scenarios. However, it is easy to make mistakes on some hard cases with small tumor areas considering the extensively high-resolution of whole slide images (WSIs). The absence of fine-grained and large-scale annotations of such small tumor lesions makes this problem more challenging. Existing methods usually perform uniform cropping of the foreground of WSI and then use convolutional neural networks as the backbone network to predict the classification results. However, cropping can damage the structure of tiny tumors, which affects classification accuracy. To solve this problem, we propose an Intensive-Sampling Multiple Instance Learning Framework (ISMIL), which focuses on tumor regions and improves the recognition of small tumor regions by intensively sampling the crucial regions. Experiments of prostate cancer detection show that our method achieves an area under the receiver operating characteristic curve (AUC) of 0.987 on the PANDA sets, which improves recall by at least 33% with higher specificity over the current primary methods for hard cases. The ISMIL also demonstrates comparable abilities to human experts on the prostate cancer grading task. Moreover, ISMIL has shown good robustness in independent cohorts, which makes it a potential tool to improve the diagnostic efficiency of pathologists.

  View details for DOI 10.1007/s00428-023-03502-z

  View details for Web of Science ID 000937707800001

  View details for PubMedID 36823229

  View details for PubMedCentralID 8067693

• Towards Real-Time Neural Video Codec for Cross-Platform Application Using Calibration Information Tian, K., Guan, Y., Xiang, J., Zhang, J., Han, X., Yang, W., ACM ASSOC COMPUTING MACHINERY. 2023: 7961-7970

  View details for DOI 10.1145/3581783.3611955

  View details for Web of Science ID 001199449107094

• Automatic diagnosis and grading of Prostate Cancer with weakly supervised learning on whole slide images COMPUTERS IN BIOLOGY AND MEDICINE Xiang, J., Wang, X., Wang, X., Zhang, J., Yang, S., Yang, W., Han, X., Liu, Y. 2023; 152: 106340

  Abstract

  The workflow of prostate cancer diagnosis and grading is cumbersome and the results suffer from substantial inter-observer variability. Recent trials have shown potential in using machine learning to develop automated systems to address this challenge. Most automated deep learning systems for prostate cancer Gleason grading focused on supervised learning requiring demanding fine-grained pixel-level annotations.A weakly-supervised deep learning model with slide-level labels is presented in this study for the diagnosis and grading of prostate cancer with whole slide image (WSI). WSIs are first cropped into small patches and then processed with a deep learning model to extract patch-level features. A graph convolution network (GCN) is used to aggregate the features for classifications. Throughout the training process, the noisy labels are progressively filtered out to reduce inter-observer variations in clinical reports. Finally, multi-center independent test cohorts with 6,174 slides are collected to evaluate the prostate cancer diagnosis and grading performance of our model.The cancer diagnosis (2-level classification) results on two external test sets (n= 4,675, n= 844) show an area under the receiver operating characteristic curve (AUC) of 0.985 and 0.986. The Gleason grading (6-level classification) results reach 0.931 quadratic weighted kappa on the internal test set (n= 531). It generalizes well on the external test dataset (n= 844) with 0.801 quadratic weighted kappa with the reference standard set independently. The model enables pathological meaningful interpretability by visualizing the most attended lesions which are highly consistent with expert annotations.The proposed model incorporates a graph network in weakly supervised learning with only slide-level reports. A robust learning strategy is also employed to correct the label noise. It is highly accurate (>0.985 AUC for diagnosis) and also interpretable with intuitive heatmap visualization. It can be unified with a digital pathology pipeline to deliver prostate cancer metrics for a pathology report.

  View details for DOI 10.1016/j.compbiomed.2022.106340

  View details for Web of Science ID 000903180300002

  View details for PubMedID 36481762

• MMV-Net: A Multiple Measurement Vector Network for Multifrequency Electrical Impedance Tomography IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS Chen, Z., Xiang, J., Bagnaninchi, P., Yang, Y. 2023; 34 (11): 8938-8949

  Abstract

  Multifrequency electrical impedance tomography (mfEIT) is an emerging biomedical imaging modality to reveal frequency-dependent conductivity distributions in biomedical applications. Conventional model-based image reconstruction methods suffer from low spatial resolution, unconstrained frequency correlation, and high computational cost. Deep learning has been extensively applied in solving the EIT inverse problem in biomedical and industrial process imaging. However, most existing learning-based approaches deal with the single-frequency setup, which is inefficient and ineffective when extended to the multifrequency setup. This article presents a multiple measurement vector (MMV) model-based learning algorithm named MMV-Net to solve the mfEIT image reconstruction problem. MMV-Net considers the correlations between mfEIT images and unfolds the update steps of the Alternating Direction Method of Multipliers for the MMV problem (MMV-ADMM). The nonlinear shrinkage operator associated with the weighted l2,1 regularization term of MMV-ADMM is generalized in MMV-Net with a cascade of a Spatial Self-Attention module and a Convolutional Long Short-Term Memory (ConvLSTM) module to better capture intrafrequency and interfrequency dependencies. The proposed MMV-Net was validated on our Edinburgh mfEIT Dataset and a series of comprehensive experiments. The results show superior image quality, convergence performance, noise robustness, and computational efficiency against the conventional MMV-ADMM and the state-of-the-art deep learning methods.

  View details for DOI 10.1109/TNNLS.2022.3154108

  View details for Web of Science ID 000767800400001

  View details for PubMedID 35263263

• Node-aligned Graph Convolutional Network for Whole-slide Image Representation and Classification Guan, Y., Zhang, J., Tian, K., Yang, S., Dong, P., Xiang, J., Yang, W., Huang, J., Zhang, Y., Han, X., IEEE COMP SOC IEEE COMPUTER SOC. 2022: 18791-18801

  View details for DOI 10.1109/CVPR52688.2022.01825

  View details for Web of Science ID 000870783004060

• FISTA-Net: Learning a Fast Iterative Shrinkage Thresholding Network for Inverse Problems in Imaging IEEE TRANSACTIONS ON MEDICAL IMAGING Xiang, J., Dong, Y., Yang, Y. 2021; 40 (5): 1329-1339

  Abstract

  Inverse problems are essential to imaging applications. In this letter, we propose a model-based deep learning network, named FISTA-Net, by combining the merits of interpretability and generality of the model-based Fast Iterative Shrinkage/Thresholding Algorithm (FISTA) and strong regularization and tuning-free advantages of the data-driven neural network. By unfolding the FISTA into a deep network, the architecture of FISTA-Net consists of multiple gradient descent, proximal mapping, and momentum modules in cascade. Different from FISTA, the gradient matrix in FISTA-Net can be updated during iteration and a proximal operator network is developed for nonlinear thresholding which can be learned through end-to-end training. Key parameters of FISTA-Net including the gradient step size, thresholding value and momentum scalar are tuning-free and learned from training data rather than hand-crafted. We further impose positive and monotonous constraints on these parameters to ensure they converge properly. The experimental results, evaluated both visually and quantitatively, show that the FISTA-Net can optimize parameters for different imaging tasks, i.e. Electromagnetic Tomography (EMT) and X-ray Computational Tomography (X-ray CT). It outperforms the state-of-the-art model-based and deep learning methods and exhibits good generalization ability over other competitive learning-based approaches under different noise levels.

  View details for DOI 10.1109/TMI.2021.3054167

  View details for Web of Science ID 000645866500003

  View details for PubMedID 33493113

• Multi-Frequency Electromagnetic Tomography for Acute Stroke Detection Using Frequency-Constrained Sparse Bayesian Learning IEEE TRANSACTIONS ON MEDICAL IMAGING Xiang, J., Dong, Y., Yang, Y. 2020; 39 (12): 4102-4112

  Abstract

  Imaging the bio-impedance distribution of the brain can provide initial diagnosis of acute stroke. This paper presents a compact and non-radiative tomographic modality, i.e. multi-frequency Electromagnetic Tomography (mfEMT), for the initial diagnosis of acute stroke. The mfEMT system consists of 12 channels of gradiometer coils with adjustable sensitivity and excitation frequency. To solve the image reconstruction problem of mfEMT, we propose an enhanced Frequency-Constrained Sparse Bayesian Learning (FC-SBL) to simultaneously reconstruct the conductivity distribution at all frequencies. Based on the Multiple Measurement Vector (MMV) model in the Sparse Bayesian Learning (SBL) framework, FC-SBL can recover the underlying distribution pattern of conductivity among multiple images by exploiting the frequency constraint information. A realistic 3D head model was established to simulate stroke detection scenarios, showing the capability of mfEMT to penetrate the highly resistive skull and improved image quality with FC-SBL. Both simulations and experiments showed that the proposed FC-SBL method is robust to noisy data for image reconstruction problems of mfEMT compared to the single measurement vector model, which is promising to detect acute strokes in the brain region with enhanced spatial resolution and in a baseline-free manner.

  View details for DOI 10.1109/TMI.2020.3013100

  View details for Web of Science ID 000595547500031

  View details for PubMedID 32746151

• Electronics of a Wearable ECG With Level Crossing Sampling and Human Body Communication IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS Xiang, J., Dong, Y., Xue, X., Xiong, H. 2019; 13 (1): 68-79

  Abstract

  In this paper, the human body communication (HBC) and level crossing sampling (LCS) are combined to design electronics for a wearable electrocardiograph (ECG). The ECG signals acquired by capacitively coupled electrodes are sampled with LCS in place of conventional synchronous sampling. In order to transmit signals through HBC at low frequencies (100 kHz, 1 MHz), an electric field sensor with high input impedance is adopted as the front end of the HBC receiver. The HBC channel gain is enhanced by more than 30 dB with the electric field sensor. An LCS structure based on the send-on-delta concept is implemented with discrete components to convert the ECG signals into binary impulses. The converted impulses are modulated by an on-off keying modulator and then transmitted via the human body to the receiver. A prototype ECG waist belt is developed with commercially available components and experimentally evaluated. The results indicate that the acquired ECG waveforms exhibit good agreement with regular Ag/AgCl ECG methods. The heartbeat detection using a technique based on the Kadane's algorithm and the power consumption performance of the proposed system are also discussed.

  View details for DOI 10.1109/TBCAS.2018.2879818

  View details for Web of Science ID 000457794600006

  View details for PubMedID 30418883

• Design of a Magnetic Induction Tomography System by Gradiometer Coils for Conductive Fluid Imaging IEEE ACCESS Xiang, J., Dong, Y., Zhang, M., Li, Y. 2019; 7: 56733-56744

  View details for DOI 10.1109/ACCESS.2019.2914377

  View details for Web of Science ID 000468489100001

• Capturing Electrocardiogram Signals from Chairs by Multiple Capacitively Coupled Unipolar Electrodes SENSORS Hou, Z., Xiang, J., Dong, Y., Xue, X., Xiong, H., Yang, B. 2018; 18 (9)

  Abstract

  A prototype of an electrocardiogram (ECG) signal acquisition system with multiple unipolar capacitively coupled electrodes is designed and experimentally tested. Capacitively coupled electrodes made of a standard printed circuit board (PCB) are used as the sensing electrodes. Different from the conventional measurement schematics, where one single lead ECG signal is acquired from a pair of sensing electrodes, the sensing electrodes in our approaches operate in a unipolar mode, i.e., the biopotential signals picked up by each sensing electrodes are amplified and sampled separately. Four unipolar electrodes are mounted on the backrest of a regular chair and therefore four channel of signals containing ECG information are sampled and processed. It is found that the qualities of ECG signal contained in the four channel are different from each other. In order to pick up the ECG signal, an index for quality evaluation, as well as for aggregation of multiple signals, is proposed based on phase space reconstruction. Experimental tests are carried out while subjects sitting on the chair and clothed. The results indicate that the ECG signals can be reliably obtained in such a unipolar way.

  View details for DOI 10.3390/s18092835

  View details for Web of Science ID 000446940600094

  View details for PubMedID 30154303

  View details for PubMedCentralID PMC6163948

• A Real-Time QRS Detection Method Based on Phase Portraits and Box-Scoring Calculation IEEE SENSORS JOURNAL Hou, Z., Dong, Y., Xiang, J., Li, X., Yang, B. 2018; 18 (9): 3694-3702

  View details for DOI 10.1109/JSEN.2018.2812792

  View details for Web of Science ID 000429694400027