Cancer Survival Prognosis From Whole Slide Images Using Hopfield Network

In this study, we propose utilizing the Hopfield network for cancer survival prognosis using Whole Slide Image (WSI) datasets. WSIs, known for their high-resolution histological details, are increasingly adopted in cancer diagnostics but pose challenges due to their massive data size and the need for both efficiency and precision in predictive models. By leveraging the associative memory capabilities of the Hopfield network, we model the complex relationships between histological features in WSIs and patient survival outcomes. Experimental results on specific dataset, e.g., TCGA, demonstrate that the Hopfield network outperforms traditional machine learning models, such as specific examples, e.g., random forests or support vector machines, and achieves comparable or superior accuracy to state-of-the-art deep learning approaches, with significantly lower computational overhead. These findings underscore the potential of the Hopfield network as an effective and interpretable tool for survival prognosis, opening promising directions for future research in medical image analysis and personalized medicine.