Multi-modal Coss-omics Deep Learning for Pan-cancer Analysis

用于泛癌分析的多模态跨组学的统一的深度学习框架，持续更新中。并对最新相关文章进行汇总。

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   作者测试了6种深度学习方法的性能。

2. Lee, Yongju, et al. "Derivation of prognostic contextual histopathological features from whole-slide images of tumours via graph deep learning." Nature Biomedical Engineering (2022): 1-15. [paper], [code]

   充分挖掘了病理上下文肿瘤微环境。

3. Li, Xintong, et al. "A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches." Artificial Intelligence Review (2022): 1-70. [paper]

4. Wang, Xiyue, et al. "Transformer-based unsupervised contrastive learning for histopathological image classification." Medical Image Analysis 81 (2022): 102559. [paper], [code]

   用于组织病理学图像的基于 Transformer 的无监督特征提取器。

1. Leng, Dongjin, et al. "A benchmark study of deep learning-based multi-omics data fusion methods for cancer." Genome biology 23.1 (2022): 1-32. [paper], [code]

   对多组学数据融合方法进行了系统性的研究和对比。

1. Schneider, Lucas, et al. "Integration of deep learning-based image analysis and genomic data in cancer pathology: A systematic review." European Journal of Cancer 160 (2022): 80-91. [paper]

2. Zheng, Shuai, et al. "Multi-modal Graph Learning for Disease Prediction." IEEE Transactions on Medical Imaging (2022). [paper], [code]