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Xiaobo Chen

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JBHI Journal 2026 Journal Article

BLADE: Breast Lesion Analysis with Domain Expertise for DCE-MRI Diagnosis

  • Zhitao Wei
  • Yi Dai
  • Yanting Liang
  • Chinting Wong
  • Yanfen Cui
  • Xiaobo Chen
  • Zhihe Zhao
  • Xiaodong Zheng

Dynamic Contrast-Enhanced Magnetic Reso nance Imaging (DCE-MRI) is pivotal in breast cancer diag nosis, yet radiologists face challenges in interpreting its complex data due to the lack of robust automated tools. Current lesion diagnosis systems struggle with limited datasets and insufficient integration of domain knowledge. To overcome these limitations, we propose Breast Lesion Analysis with DomainExpertise(BLADE), anoveldiagnosis framework that synergizes deep learning with clinical ex pertise. BLADE leverages a pre-trained vertical foundation model (optimized via Momentum Contrast on 2. 1 million MRI slices) as its encoder, ensuring robust feature extraction. Crucially, the system incorporates prior multi-phasic hemodynamic knowledge to emulate radiologists' diagnos tic reasoning and introduces a Breast Imaging Reporting and Data System (BI-RADS)-based constraint during training to align predictions with clinical standards. Extensive experiments demonstrate that BLADE outperforms state of-the-art methods, achieving an Area Under the Curve (AUC) of 0. 9228 and 0. 9553 on two external test datasets, respectively. Notably, BLADE significantly enhances clin ical workflow; when used as an assistive tool, BLADE improves diagnostic accuracy by 14. 31%, surpassing stan daloneperformanceofclinicians. This workbridgesthegap between AI-driven analysis and clinical practice in breast MRI interpretation. The source code is available at https://github.com/GDPHMediaLab/BLADE.

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AAAI Conference 2018 Conference Paper

Multi-Layer Multi-View Classification for Alzheimer’s Disease Diagnosis

  • Changqing Zhang
  • Ehsan Adeli
  • Tao Zhou
  • Xiaobo Chen
  • Dinggang Shen

In this paper, we propose a novel multi-view learning method for Alzheimer’s Disease (AD) diagnosis, using neuroimaging and genetics data. Generally, there are several major challenges associated with traditional classification methods on multi-source imaging and genetics data. First, the correlation between the extracted imaging features and class labels is generally complex, which often makes the traditional linear models ineffective. Second, medical data may be collected from different sources (i. e. , multiple modalities of neuroimaging data, clinical scores or genetics measurements), therefore, how to effectively exploit the complementarity among multiple views is of great importance. In this paper, we propose a Multi-Layer Multi-View Classification (ML-MVC) approach, which regards the multi-view input as the first layer, and constructs a latent representation to explore the complex correlation between the features and class labels. This captures the high-order complementarity among different views, as we exploit the underlying information with a low-rank tensor regularization. Intrinsically, our formulation elegantly explores the nonlinear correlation together with complementarity among different views, and thus improves the accuracy of classification. Finally, the minimization problem is solved by the Alternating Direction Method of Multipliers (ADMM). Experimental results on Alzheimers Disease Neuroimaging Initiative (ADNI) data sets validate the effectiveness of our proposed method.

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