319 citations · 377 across the 8 of their papers we have counts for
10 papers
Towards Scalable Context-Aware Single-Cell Spatial Transcriptomics Prediction from Histology Images
Zijun Gao, Chunbin Gu, Jinxi Xiang +2
Predicting gene expression from H&E-stained histology images offers a scalable alternative to costly spatial transcriptomics, yet most existing methods operate at the spot level, w…
A Generative Foundation Model for Multimodal Histopathology
Jinxi Xiang, Mingjie Li, Siyu Hou +9
Accurate diagnosis and treatment of complex diseases require integrating histological, molecular, and clinical data, yet in practice these modalities are often incomplete owing to…
A Multimodal Foundation Model of Spatial Transcriptomics and Histology for Biological Discovery and Clinical Prediction
Jinxi Xiang, Siyu Hou, Yuchen Li +17
Spatial transcriptomics (ST) enables gene expression mapping within anatomical context but remains costly and low-throughput. Hematoxylin and eosin (H\&E) staining offers rich morp…
nnMIL: A generalizable multiple instance learning framework for computational pathology
Xiangde Luo, Jinxi Xiang, Yuanfeng Ji +1
Computational pathology holds substantial promise for improving diagnosis and guiding treatment decisions. Recent pathology foundation models enable the extraction of rich patch-le…
Artificial intelligence for diagnosing and predicting survival of patients with renal cell carcinoma: Retrospective multi-center study
Siteng Chen, Xiyue Wang, Jun Zhang +8
Background: Clear cell renal cell carcinoma (ccRCC) is the most common renal-related tumor with high heterogeneity. There is still an urgent need for novel diagnostic and prognosti…
A Deep Learning Framework for Nuclear Segmentation and Classification in Histopathological Images
Sen Yang, Jinxi Xiang, Xiyue Wang
Nucleus segmentation and classification are the prerequisites in the workflow of digital pathology processing. However, it is very challenging due to its high-level heterogeneity a…
MMV-Net: A Multiple Measurement Vector Network for Multi-frequency Electrical Impedance Tomography
Zhou Chen, Jinxi Xiang, Pierre Bagnaninchi +1
Multi-frequency Electrical Impedance Tomography (mfEIT) is an emerging biomedical imaging modality to reveal frequency-dependent conductivity distributions in biomedical applicatio…
FISTA-Net: Learning A Fast Iterative Shrinkage Thresholding Network for Inverse Problems in Imaging
Jinxi Xiang, Yonggui Dong, Yunjie Yang
Inverse problems are essential to imaging applications. In this paper, we propose a model-based deep learning network, named FISTA-Net, by combining the merits of interpretability…
Image Reconstruction for Multi-frequency Electromagnetic Tomography based on Multiple Measurement Vector Model
Jinxi Xiang, Zhou Chen, Yonggui Dong +1
Imaging the bio-impedance distribution of a biological sample can provide understandings about the sample's electrical properties which is an important indicator of physiological s…
Multi-frequency Electromagnetic Tomography for Acute Stroke Detection Using Frequency Constrained Sparse Bayesian Learning
Jinxi Xiang, Yonggui Dong, Yunjie Yang
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-…