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Lizhi Shao

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5 papers

1 author row

AIIM Journal 2026 Journal Article

Context-aware heterogeneous graph neural network for multi-level description and invasiveness prediction in renal cell carcinoma

Xiaoming Jiang
Guoying Ji
Ye Yan
Xiongjun Ye
Chao Liang
Bao Li
Wei Wang
Shudong Zhang

The invasiveness prediction in renal cell carcinoma (RCC) is of significant importance for the decision of clinical surgical plans and the patients' prognosis. Currently, besides invasive pathological assessment, it mainly relies on observation through computed tomography (CT) imaging. However, limitations of human vision and qualitative descriptions restrict the accuracy of the diagnosis of renal sinus invasion (RSI). Recently, artificial intelligence approaches have shown promising prospects in cancer diagnosis. Due to the complex imaging characteristics of invasiveness, prediction models that only focus on tumor regions are inadequate, requiring comprehensive evaluation of intratumoral heterogeneity, peritumoral information, and the kidney in which the tumor resides. Therefore, in this study, we propose a context-aware heterogeneous graph neural network for multi-level description and invasiveness prediction in RCC. The superiority of the proposed model lies in its ability to integrate imaging features at multi-level, and to learn disturbance invariant features through a data-driven diffusion perturbation strategy. To evaluate the effectiveness and generalization of our model, we conduct extensive experiments on a multi-center dataset (including CT scan images of 437 patients) to compare our model with a series of state-of-the-art (SOTA) classification models. The experimental results show the superiority of our model for RSI classification ( AUC = 0. 88 ). Additionally, we also perform a comparative study with clinical experts, and the proposed method is significantly better than existing assessment methods and clinical experts ( p < 0. 05 ). In general, our work provides an effective assessment tool for automated diagnosis of RSI in RCC and also offers new insights for constructing more precise tumor prediction models.

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EAAI Journal 2025 Journal Article

Dual-domain contrastive learning for three-dimensional multi-parametric magnetic resonance imaging to end-to-end predict kidney cancer subtypes

Guoying Ji
Lizhi Shao
Yihao Zhu
Xuwen Li
Tianwang Xun
Junxian Wu
Yabo Zhai
Yuan Yuan

Prediction of subtypes is important for clinical decision-making in kidney cancer. Multi-parametric magnetic resonance imaging (mp-MRI) provides a non-invasive way to evaluate tumor characteristics. However, due to the heterogeneity of pixel, modality, and objective representation, the computer-aided diagnosis of subtypes is challenging. In this study, we propose a novel diagnosis framework for kidney cancer subtypes based on mp-MRI, dual-domain contrastive learning network (DCLNet), which has two innovations: (i) the dual-domain contrastive learning scheme based on intra-case consistency and inter-case specificity that mines the correlation and diversity of dual-domain (T1-weighted and T2-weighted) image information, and (ii) the linear diffusion augmentation strategy that enriches training data in three-dimensional image sparse representation and increases the robustness of features. In experiments, a real-world dataset from multiple centers is established for the development and validation of DCLNet. The proposed method yields multiple classification accuracy of 75. 49 % for kidney cancer subtypes. The area under the curve for the aggressive malignant tumor clear cell renal cell carcinoma and the benign tumor angiomyolipoma is 89. 53 % and 88. 95 %, respectively. Significantly, our proposed method demonstrates significant improvement over state-of-the-art methods (p < 0. 01). This study offers a reliable model for non-invasive prediction of kidney cancer subtypes. It also shows potential to overcome multi-source heterogeneity and improve performance in cancer classification. Our code is available at https: //github. com/xiaojidream/DCLNet.

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EAAI Journal 2024 Journal Article

Federated learning with comparative learning-based dynamic parameter updating on glioma whole slide images

Longjian Huang
Lizhi Shao
Meiling Bao
Changsong Guo
Zhuhong Shao
Xiazi Huang
Mingjing Wang
Xiaoming Jiang

The rapid advancements in artificial intelligence have profoundly impacted various societal domains, particularly in healthcare. In computational pathology, deep learning techniques have shown remarkable abilities in classifying, segmenting, and recognizing pathology images. However, acquiring large-scale, high-quality medical datasets has become challenging due to increased privacy concerns and data protection awareness among institutions and patients. We propose utilizing federated learning to address this data privacy issue in this study. Our research focuses on classifying glioma whole slide images. To enhance the privacy of sensitive data, we incorporate Laplace noise into the model parameters of each local client. This technique guarantees the protection of patients’ data while allowing collaborative learning. Moreover, we introduce a novel method called Federated Learning with Comparative Learning-based Dynamic Parameter Updating. We select a local model with the optimal performance before all local model parameters are aggregated into global model parameters. Other local models then learn to update parameters from this selected model. By incorporating the Comparative Learning-based Dynamic Parameter Updating, we enhance the learning effect and improve the overall model performance for classifying glioma data. To assess our proposed method, we perform assessments on two separate classification tasks. The results of our experiments show that our privacy-preserving federated learning framework effectively utilizes multi-center data while maintaining good privacy protection performance. Additionally, compared to the commonly used federated averaging baseline method, our approach significantly outperforms glioma data classification tasks. Our research offers a promising framework that achieves high classification accuracy and ensures the protection of sensitive medical data, thus showcasing its potential in advancing computational pathology research and practice. Our code is free at https: //github. com/jiaxian-hlj/FL-Dpu.

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NeurIPS Conference 2024 Conference Paper

Leveraging Tumor Heterogeneity: Heterogeneous Graph Representation Learning for Cancer Survival Prediction in Whole Slide Images

Junxian Wu
Xinyi Ke
Xiaoming Jiang
Huanwen Wu
Youyong Kong
Lizhi Shao

Survival prediction is a significant challenge in cancer management. Tumor micro-environment is a highly sophisticated ecosystem consisting of cancer cells, immune cells, endothelial cells, fibroblasts, nerves and extracellular matrix. The intratumor heterogeneity and the interaction across multiple tissue types profoundly impacts the prognosis. However, current methods often neglect the fact that the contribution to prognosis differs with tissue types. In this paper, we propose ProtoSurv, a novel heterogeneous graph model for WSI survival prediction. The learning process of ProtoSurv is not only driven by data but also incorporates pathological domain knowledge, including the awareness of tissue heterogeneity, the emphasis on prior knowledge of prognostic-related tissues, and the depiction of spatial interaction across multiple tissues. We validate ProtoSurv across five different cancer types from TCGA (i. e. , BRCA, LGG, LUAD, COAD and PAAD), and demonstrate the superiority of our method over the state-of-the-art methods.

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AAAI Conference 2024 Conference Paper

Point Transformer with Federated Learning for Predicting Breast Cancer HER2 Status from Hematoxylin and Eosin-Stained Whole Slide Images

Bao Li
Zhenyu Liu
Lizhi Shao
Bensheng Qiu
Hong Bu
Jie Tian

Directly predicting human epidermal growth factor receptor 2 (HER2) status from widely available hematoxylin and eosin (HE)-stained whole slide images (WSIs) can reduce technical costs and expedite treatment selection. Accurately predicting HER2 requires large collections of multi-site WSIs. Federated learning enables collaborative training of these WSIs without gigabyte-size WSIs transportation and data privacy concerns. However, federated learning encounters challenges in addressing label imbalance in multi-site WSIs from the real world. Moreover, existing WSI classification methods cannot simultaneously exploit local context information and long-range dependencies in the site-end feature representation of federated learning. To address these issues, we present a point transformer with federated learning for multi-site HER2 status prediction from HE-stained WSIs. Our approach incorporates two novel designs. We propose a dynamic label distribution strategy and an auxiliary classifier, which helps to establish a well-initialized model and mitigate label distribution variations across sites. Additionally, we propose a farthest cosine sampling based on cosine distance. It can sample the most distinctive features and capture the long-range dependencies. Extensive experiments and analysis show that our method achieves state-of-the-art performance at four sites with a total of 2687 WSIs. Furthermore, we demonstrate that our model can generalize to two unseen sites with 229 WSIs. Code is available at: https://github.com/boyden/PointTransformerFL

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