Hierarchical multimodal fusion framework based on noisy label learning and attention mechanism for cancer classification with pathology and genomic features
Abstract
Classification of subtype and grade is imperative in the clinical diagnosis and prognosis of cancer. Many deep learning-based studies related to cancer classification are based on pathology and genomics. However, most of them are late fusion-based and require full supervision in pathology image analysis. To address these problems, we present an integrated framework for cancer classification with pathology and genomics data. This framework consists of two major parts, a weakly supervised model for extracting patch features from whole slide images (WSIs), and a hierarchical multimodal fusion model. The weakly supervised model can make full use of WSI labels, and mitigate the effects of label noises by the self-training strategy. The generic multimodal fusion model is capable of capturing deep interaction information through multi-level attention mechanisms and controlling the expressiveness of each modal representation. We validate our approach on glioma and lung cancer datasets from The Cancer Genome Atlas (TCGA). The results demonstrate that the proposed method achieves superior performance compared to state-of-the-art methods, with the competitive AUC of 0.872 and 0.977 on these two datasets respectively. This paper establishes insight on how to build deep networks on multimodal biomedical data and proposes a more general framework for pathology image analysis without pixel-level annotation.
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Qiu et al. introduced a weakly supervised model PathIn-NL, which included an attention-based hierarchical multimodal fusion module named AHM-Fusion, providing an effective approach to better represent the WSI features and perform information fusions in multimodal learning.
“Qiu et al. introduced a weakly supervised model PathIn-NL, which included an attention-based hierarchical multimodal fusion module named AHM-Fusion, providing an effective approach to better represent the WSI features and perform information fusions in multimodal learning [99].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [99]
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Influential
- Authors
- Qiu, L., Zhao, L., Hou, R., Zhao, W., Zhang, S., Lin, Z., Teng, H., & Zhao, J.
- Affiliations
- Shanghai Jiao Tong University (CN)
- Shanghai Chest Hospital (CN)
- Corresponding author
- Zhao, Lu
- Zhao, Wangyuan
- Zhao, Jun
- Venue
- Computerized Medical Imaging and Graphics
- Publisher
- Elsevier BV
- Published
- 2023-03
- Volume
- 104
- Pages
- 102176
- ISSN
- 0730-4862 (ISSN-L)
- 0895-6111
- OpenAlex
- W4315490106
- Cited by
- 35
- References
- 34
- Citations by year
- 2026: 9 · 2025: 13 · 2024: 10 · 2023: 3
- FWCI
- 3.02
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Computerized Medical Imaging and Graphics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- cancer classification, multimodal fusion, whole slide images, pathology images, genomic features, noisy label learning, weakly supervised learning, self-training, hierarchical attention mechanism, glioma, lung cancer, TCGA
- MeSH terms
- Glioma, Humans, Image Processing, Computer-Assisted, Lung Neoplasms, Genomics
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