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Shijie Deng

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ICML Conference 2024 Conference Paper

Diffusion-based Missing-view Generation With the Application on Incomplete Multi-view Clustering

  • Jie Wen 0001
  • Shijie Deng
  • Waikeung Wong
  • Guoqing Chao
  • Chao Huang 0008
  • Lunke Fei
  • Yong Xu 0001

As a branch of clustering, multi-view clustering has received much attention in recent years. In practical applications, a common phenomenon is that partial views of some samples may be missing in the collected multi-view data, which poses a severe challenge to design the multi-view learning model and explore complementary and consistent information. Currently, most of the incomplete multi-view clustering methods only focus on exploring the information of available views while few works study the missing view recovery for incomplete multi-view learning. To this end, we propose an innovative diffusion-based missing view generation (DMVG) network. Moreover, for the scenarios with high missing rates, we further propose an incomplete multi-view data augmentation strategy to enhance the recovery quality for the missing views. Extensive experimental results show that the proposed DMVG can not only accurately predict missing views, but also further enhance the subsequent clustering performance in comparison with several state-of-the-art incomplete multi-view clustering methods.

Details

AIIM Journal 2024 Journal Article

Improving diagnosis and outcome prediction of gastric cancer via multimodal learning using whole slide pathological images and gene expression

  • Yuzhang Xie
  • Qingqing Sang
  • Qian Da
  • Guoshuai Niu
  • Shijie Deng
  • Haoran Feng
  • Yunqin Chen
  • Yuan-Yuan Li

For the diagnosis and outcome prediction of gastric cancer (GC), machine learning methods based on whole slide pathological images (WSIs) have shown promising performance and reduced the cost of manual analysis. Nevertheless, accurate prediction of GC outcome may rely on multiple modalities with complementary information, particularly gene expression data. Thus, there is a need to develop multimodal learning methods to enhance prediction performance. In this paper, we collect a dataset from Ruijin Hospital and propose a multimodal learning method for GC diagnosis and outcome prediction, called GaCaMML, which is featured by a cross-modal attention mechanism and Per-Slide training scheme. Additionally, we perform feature attribution analysis via integrated gradient (IG) to identify important input features. The proposed method improves prediction accuracy over the single-modal learning method on three tasks, i. e. , survival prediction (by 4. 9% on C-index), pathological stage classification (by 11. 6% on accuracy), and lymph node classification (by 12. 0% on accuracy). Especially, the Per-Slide strategy addresses the issue of a high WSI-to-patient ratio and leads to much better results compared with the Per-Person training scheme. For the interpretable analysis, we find that although WSIs dominate the prediction for most samples, there is still a substantial portion of samples whose prediction highly relies on gene expression information. This study demonstrates the great potential of multimodal learning in GC-related prediction tasks and investigates the contribution of WSIs and gene expression, respectively, which not only shows how the model makes a decision but also provides insights into the association between macroscopic pathological phenotypes and microscopic molecular features.

Details DOI
v2026.09.13